arXiv subjects

Fan Xu

Publications and source records attributed to Fan Xu.

At least 127 records · Page 7Linked to original sources

arXiv2024

Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum

Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. In recent years, Graph Neural Networks (GNN) have been widely applied to GFD, characterizing the anomalous possibility of a node by aggregating neighbor information. However, fraud graphs are inherently heterophilic, thus most of GNNs perform poorly due to their assumption of homophily. In addition, due to the existence of heterophily and class imbalance problem, the existing models do not fully utilize the precious node label information. To address the above issues, this paper proposes a semi-supervised GNN-based fraud detector SEC-GFD. This detector includes a hybrid filtering module and a local environmental constraint module, the two modules are utilized to solve heterophily and label utilization problem respectively. The first module starts from the perspective of the spectral domain, and solves the heterophily problem to a certain extent. Specifically, it divides the spectrum into various mixed-frequency bands based on the correlation between spectrum energy distribution and heterophily. Then in order to make full use of the node label information, a local environmental constraint module is adaptively designed. The comprehensive experimental results on four real-world fraud detection datasets denote that SEC-GFD outperforms other competitive graph-based fraud detectors. We release our code at https://github.com/Sunxkissed/SEC-GFD.

cs.LG↗

arXiv2024

GLADformer: A Mixed Perspective for Graph-level Anomaly Detection

Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph dataset. However, current methods are constrained by their receptive fields, struggling to learn global features within the graphs. Moreover, most contemporary methods are based on spatial domain and lack exploration of spectral characteristics. In this paper, we propose a multi-perspective hybrid graph-level anomaly detector namely GLADformer, consisting of two key modules. Specifically, we first design a Graph Transformer module with global spectrum enhancement, which ensures balanced and resilient parameter distributions by fusing global features and spectral distribution characteristics. Furthermore, to uncover local anomalous attributes, we customize a band-pass spectral GNN message passing module that further enhances the model's generalization capability. Through comprehensive experiments on ten real-world datasets from multiple domains, we validate the effectiveness and robustness of GLADformer. This demonstrates that GLADformer outperforms current state-of-the-art models in graph-level anomaly detection, particularly in effectively capturing global anomaly representations and spectral characteristics.

cs.LG↗

arXiv2024

Tunable superconductivity in electron- and hole-doped Bernal bilayer graphene

Graphene-based, high quality two-dimensional electronic systems have emerged as a highly tunable platform for studying superconductivity. Specifically, superconductivity has been observed in both electron-doped and hole-doped twisted graphene moire systems, whereas in crystalline graphene systems, superconductivity has so far only been observed in hole-doped rhombohedral trilayer and hole-doped Bernal bilayer graphene (BBG). Recently, enhanced superconductivity has been demonstrated in BBG due to the proximity with a monolayer WSe2. Here, we report the observation of superconductivity and a series of flavor-symmetry-breaking phases in both electron- and hole-doped BBG/WSe2 device by electrostatic doping. The strength of the observed superconductivity is tunable by applied vertical electric fields. The maximum Berezinskii-Kosterlitz-Thouless (BKT) transition temperature for the electron- and hole-doped superconductivity is about 210 mK and 400 mK, respectively. Superconductivities emerge only when applied electric fields drive BBG electron or hole wavefunctions toward the WSe2 layer, underscoring the importance of the WSe2 layer in the observed superconductivity. We find the hole-doped superconductivity violates the Pauli paramagnetic limit, consistent with an Ising-like superconductor. In contrast, the electron-doped superconductivity obeys the Pauli limit, even though the proximity induced Ising spin-orbit coupling is also notable in the conduction band. Our findings highlight the rich physics associated with the conduction band in BBG, paving the way for further studies into the superconducting mechanisms of crystalline graphene and the development of novel superconductor devices based on BBG.

cond-mat.supr-con↗

arXiv2024

Reversible optical isolators and quasi-circulators using a magneto-optical Fabry-Pérot cavity

Nonreciprocal optical devices are essential for laser protection, modern optical communication and quantum information processing by enforcing one-way light propagation. The conventional Faraday magneto-optical nonreciprocal devices rely on a strong magnetic field, which is provided by a permanent magnet. As a result, the isolation direction of such devices is fixed and severely restricts their applications in quantum networks.In this work, we experimentally demonstrate the simultaneous one-way transmission and unidirectional reflection by using a magneto-optical Fabry-Pérot cavity and a magnetic field strength of $50~\milli\tesla$. An optical isolator and a three-port quasi-circulator are realized based on this nonreciprocal cavity system. The isolator achieves an isolation ratio of up to $22~\deci\bel$ and an averaged insertion loss down to $0.97~\deci\bel$. The quasi-circulator is realized with a fidelity exceeding $99\%$ and an overall survival probability of $89.9\%$, corresponding to an insertion loss of $\sim 0.46~\deci\bel$. The magnetic field is provided by an electromagnetic coil, thereby allowing for reversing the light circulating path. The reversible quasi-circulator paves the way for building reconfigurable quantum networks.

physics.optics↗

arXiv2024

LTRDetector: Exploring Long-Term Relationship for Advanced Persistent Threats Detection

Advanced Persistent Threat (APT) is challenging to detect due to prolonged duration, infrequent occurrence, and adept concealment techniques. Existing approaches primarily concentrate on the observable traits of attack behaviors, neglecting the intricate relationships formed throughout the persistent attack lifecycle. Thus, we present an innovative APT detection framework named LTRDetector, implementing an end-to-end holistic operation. LTRDetector employs an innovative graph embedding technique to retain comprehensive contextual information, then derives long-term features from these embedded provenance graphs. During the process, we compress the data of the system provenance graph for effective feature learning. Furthermore, in order to detect attacks conducted by using zero-day exploits, we captured the system's regular behavior and detects abnormal activities without relying on predefined attack signatures. We also conducted extensive evaluations using five prominent datasets, the efficacy evaluation of which underscores the superiority of LTRDetector compared to existing state-of-the-art techniques.

cs.CR↗

arXiv2024

SLFNet: Generating Semantic Logic Forms from Natural Language Using Semantic Probability Graphs

Building natural language interfaces typically uses a semantic parser to parse the user's natural language and convert it into structured \textbf{S}emantic \textbf{L}ogic \textbf{F}orms (SLFs). The mainstream approach is to adopt a sequence-to-sequence framework, which requires that natural language commands and SLFs must be represented serially. Since a single natural language may have multiple SLFs or multiple natural language commands may have the same SLF, training a sequence-to-sequence model is sensitive to the choice among them, a phenomenon recorded as "order matters". To solve this problem, we propose a novel neural network, SLFNet, which firstly incorporates dependent syntactic information as prior knowledge and can capture the long-range interactions between contextual information and words. Secondly construct semantic probability graphs to obtain local dependencies between predictor variables. Finally we propose the Multi-Head SLF Attention mechanism to synthesize SLFs from natural language commands based on Sequence-to-Slots. Experiments show that SLFNet achieves state-of-the-art performance on the ChineseQCI-TS and Okapi datasets, and competitive performance on the ATIS dataset.

cs.CL↗

arXiv2024

Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance

This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid dynamics modeling in the field of earth sciences, aiming to achieve high-precision simulation and prediction of fluid dynamics through spatio-temporal physics awareness and parameter diffusion guidance. In the upstream stage, we design a vector quantization reconstruction module with temporal evolution characteristics, ensuring balanced and resilient parameter distribution by introducing general physical constraints. In the downstream stage, a diffusion probability network involving parameters is utilized to generate high-quality future states of fluids, while enhancing the model's generalization ability by perceiving parameters in various physical setups. Extensive experiments on multiple benchmark datasets have verified the effectiveness and robustness of the ST-PAD framework, which showcase that ST-PAD outperforms current mainstream models in fluid dynamics modeling and prediction, especially in effectively capturing local representations and maintaining significant advantages in OOD generations.

cs.LG↗

arXiv2024

Penetrative magneto-convection of a rotating Boussinesq flow in $f$-planes

In this study, we conducted a linear instability analysis of penetrative magneto-convection in rapidly rotating Boussinesq flows within tilted f-planes, under the influence of a uniform background magnetic field. We integrated wave theory and convection theory to elucidate the penetration dynamics in rotating magneto-convection. Our findings suggest that efficient penetration in rapidly rotating flows with weakly stratified stable layers at low latitudes can be attributed to the resonance of wave transmission near the interface between unstable and stable layers. In the context of strongly stratified flows, we derived the scaling relationships of penetrative distances $Δ$ with the stability parameter $δ$. Our calculation shows that, for both rotation-dominated and magnetism-dominated flows, $Δ$ obeys a scaling of $Δ\sim O(δ^{-1/2})$. In rotation-dominated flows, we noted a general decrease in penetrative distance with increased rotational effect, and a minor decrease in penetrative distance with increased latitude. When a background magnetic field is introduced, we observed a significant shift in penetrative distance as the Elsasser number $Λ$ approaches one. The penetrative distance tends to decrease when $Λ\ll 1$ and increase when $Λ\gg 1$ with the rotational effect, indicating a transition from rotation-dominated to magnetism-dominated flow. We have further investigated the impact of the background magnetic field when it is not aligned with the rotational axis. This presents a notable contrast to the case where the magnetic field is parallel to the rotational axis.

physics.flu-dyn↗

arXiv2024

Numerical study on the mechanism of drag modulation by dispersed drops in two-phase Taylor-Couette turbulence

The presence of a dispersed phase can significantly modulate the drag in turbulent systems. We derived a conserved quantity that characterizes the radial transport of azimuthal momentum in the fluid-fluid two-phase Taylor-Couette turbulence. This quantity consists of contributions from advection, diffusion, and two-phase interface, which are closely related to density, viscosity, and interfacial tension, respectively. We found that the presence of the two-phase interface consistently produces a positive contribution to the momentum transport and leads to drag enhancement, while decreasing the density and viscosity ratios of the dispersed phase to the continuous phase reduces the contribution of local advection and diffusion terms to the momentum transport, respectively, resulting in drag reduction. Therefore, we concluded that the decreased density ratio and the decreased viscosity ratio work together to compete with the presence of two-phase interface for achieving drag modulation in fluid-fluid two-phase turbulence.

physics.flu-dyn↗

arXiv2024

Motivic cluster multiplication formulas in 2-Calabi-Yau categories

We introduce a notion of motivic cluster characters via virtual Poincaré polynomials, and prove a motivic version of multiplication formulas obtained by Chen-Xiao-Xu for weighted quantum cluster characters associated to a 2-Calabi-Yau triangulated category $\mathcal{C}$ with a cluster tilting object. Furthermore, a refined form of this formula is also given. When $\mathcal{C}$ is the cluster category of an acyclic quiver, our certain refined multiplication formula is a motivic version of the multiplication formula in [International Mathematics Research Notices, rnad172(2023)].

math.RT↗

arXiv2024

Some properties of generalized cluster algebras of geometric types

We study the lower bound algebras generated by the generalized projective cluster variables of acyclic generalized cluster algebras of geometric types. We prove that this lower bound algebra coincides with the corresponding generalized cluster algebra under the coprimality condition. As a corollary, we obtain the dual PBW bases of these generalized cluster algebras. Moreover, we show that if the standard monomials of a generalized cluster algebra of geometric type are linearly independent, then the directed graph associated to the initial generalized seed of this generalized cluster algebra does not have 3-cycles.

math.RA↗

arXiv2024

Coordinating Multiple Intelligent Reflecting Surfaces without Channel Information

Conventional beamforming methods for intelligent reflecting surfaces (IRSs) or reconfigurable intelligent surfaces (RISs) typically entail the full channel state information (CSI). However, the computational cost of channel acquisition soars exponentially with the number of IRSs. To bypass this difficulty, we propose a novel strategy called blind beamforming that coordinates multiple IRSs by means of statistics without knowing CSI. Blind beamforming only requires measuring the received signal power at the user terminal for a sequence of randomly generated phase shifts across all IRSs. The main idea is to extract the key statistical quantity for beamforming by exploring only a small portion of the whole solution space of phase shifts. We show that blind beamforming guarantees a signal-to-noise ratio (SNR) boost of Theta(N^{2L}) under certain conditions, where L is the number of IRSs and N is the number of reflecting elements per IRS. The proposed conditions for achieving the optimal SNR boost of Theta(N^{4}) in a double-IRS system are much easier to satisfy than the existing ones in the literature. Most importantly, the proposed conditions can be extended to a fully general L-IRS system. The above result significantly improves upon the state of the art in the area of multi-IRS-assisted communication. Moreover, blind beamforming is justified via field tests and simulations. In particular, as shown in our field tests at 2.6 GHz, our method yields up to 17 dB SNR boost; to the best of our knowledge, this is the first time that the use of multiple IRSs gets verified in the real world.

cs.IT↗

arXiv2023

Multi-wavelength analysis of the SN-associated low-luminosity GRB 171205A

Multi-wavelength properties of the nearby Supernova(SN)-associated low-luminosity GRB 171205A are investigated in depth to constrain its physicalan origin synthetically. The pulse width is found to be correlated with energy with a power-law index of $-0.24\pm0.07 $, which is consistent with the indices of other SN/GRBs but larger than those of long GRBs. By analyzing the overall light curve of its prompt gamma-rays and X-ray plateaus simultaneously, we infer that the early X-rays together with the gamma-rays should reflect the activities of central engine while the late X-rays may be dominated by the interaction of external shocks with circumburst material. In addition, we find that the host radio flux and offset of GRB 171205A are similar to those of other nearby low-luminosity GRBs. We adopt 9 SN/GRBs with measured offset to build a relation between peak luminosity ($L_{γ,p}$) and spectral lag ($τ$) as $L_{γ,p}\proptoτ^{-1.91\pm0.33}$. The peak luminosity and the projected physical offset of both 12 SN/GRBs and 10 KN/GRBs are found to be moderately correlated, suggesting their different progenitors. The multi-wavelength afterglow fitted with a top-hat jet model indicates that the jet half-opening angle and the viewing angle of GRB 171205A are $\thicksim$ 34.4 and 41.8 degrees, respectively, which implies that the off-axis emissions are dominated by the peripheral cocoon rather than the jet core.

astro-ph.HE↗

arXiv2023

The Multiplication Formulas of Weighted Quantum Cluster Functions

By applying the property of Ext-symmetry and the affine space structure of certain fibers, we introduce the notion of weighted quantum cluster functions and prove their multiplication formulas associated to abelian categories with Ext-symmetry and 2-Calabi-Yau triangulated categories with cluster-tilting objects.

math.QA↗

arXiv2023

Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection

Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in various domains of information security, including network intrusion, financial fraud, and malicious comments, et al. Existing methods are primarily developed in an unsupervised manner due to the challenge in obtaining labeled data. For lack of guidance from prior knowledge in unsupervised manner, the identified anomalies may prove to be data noise or individual data instances. In real-world scenarios, a limited batch of labeled anomalies can be captured, making it crucial to investigate the few-shot problem in graph anomaly detection. Taking advantage of this potential, we propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a self-supervised contrastive learning strategy within and across views to capture intrinsic and transferable structural representations. Furthermore, we propose the Deep-GNN message-enhanced reconstruction module, which extensively exploits the few-shot label information and enables long-range propagation to disseminate supervision signals to deeper unlabeled nodes. This module in turn assists in the training of self-supervised contrastive learning. Comprehensive experimental results on six real-world datasets demonstrate that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies.

cs.LG↗

arXiv2023

Joint Location Sensing and Channel Estimation for IRS-Aided mmWave ISAC Systems

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly sense the target/scatterer/user positions as well as estimate the sensing and communication (SAC) channels in the considered system, we propose a two-phase transmission scheme, where the coarse and refined sensing/channel estimation (CE) results are respectively obtained in the first phase (using scanning-based IRS reflection coefficients) and second phase (using optimized IRS reflection coefficients). For each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is developed to solve the considered joint location sensing and CE problem. The proposed algorithm effectively exploits the partial overlapping structured (POS) sparsity and 2-dimensional (2D) block sparsity inherent in the SAC channels to enhance the overall performance. Based on the estimation results from the first phase, we formulate a Cramér-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem. Simulation results are provided to verify the superiority of the proposed transmission scheme and associated algorithms.

eess.SP↗

arXiv2023

Direct reduction of iron-ore with hydrogen in fluidized beds: A coarse-grained CFD-DEM-IBM study

Hydrogen metallurgy technology uses hydrogen as the reducing agent instead of carbon reduction, which is one of the important ways to reduce carbon dioxide emissions and ensure the green and sustainable development of iron and steel industry. Due to the advantages of high gas-solid contact efficiency and outstanding mass and heat transfer, direct reduction of iron ore in fluidized beds has attracted much attention. In this study, a coarse-grained CFD-DEM-IBM solver based on hybrid CPU-GPU computing is developed to simulate the direct reduction process of two kinds of iron ore with hydrogen in fluidized beds, where an unreacted shrinking core model based on multiple reaction paths is used to model the reduction reactions, a coarse-grained model and multiple GPUs enable the significant acceleration of particle computation, and the immersed boundary method (IBM) enables the use of simple mesh even in complex geometries of reactors. The predicted results of particle reduction degree are in good agreement with the experimental values, which proves the correctness of the CFD-DEM-IBM solver. In addition, the effects of reaction kinetic parameters and operating temperature on particle reduction degree are also investigated. Present study provides a method for digital design, optimization and scale-up of ironmaking reactors.

physics.flu-dyn↗

arXiv2023

Direct numerical simulation of Taylor-Couette flow with vertical asymmetric rough walls

Direct numerical simulations are performed to explore the effects of rotating direction of the vertical asymmetric rough wall on the transport properties of Taylor-Couette (TC) flow up to a Taylor number of $\textit{Ta} = 2.39 \times 10^7$. It is shown that compared to the smooth wall, the rough wall with vertical asymmetric strips can enhance the dimensionless torque \textit{Nu}$_ω$, and more importantly, at high \textit{Ta} clockwise rotation of the inner rough wall (the fluid is sheared by the steeper slope side of the strips) results in a significantly bigger torque enhancement as compared to the counter-clockwise rotation (the fluid is sheared by the smaller slope side of the strips) due to the larger convective contribution to the angular velocity flux, although the rotating direction has a negligible effect on the torque at low \textit{Ta}. The larger torque enhancement caused by the clockwise rotation of vertical asymmetric rough wall at high \textit{Ta} is then explained by the stronger coupling between the rough wall and the bulk due to the larger biased azimuthal velocity towards the rough wall at the mid-gap of TC system, the increased intensity of turbulence manifesting by larger Reynolds stress and thinner boundary layer, and the more significant contribution of the pressure force on the surface of rough wall to the torque.

physics.flu-dyn↗