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Researcher profile

Yue Tan

Yue Tan contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
hep-phMachine LearningDistributed, Parallel, and Cluster Computingeess.SPhep-exhep-latnucl-th

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Published work

8 published item(s)

preprint2026arXiv

FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection

Graph-level anomaly detection (GLAD) is crucial for ensuring the reliability of graph-driven applications by identifying abnormal graphs that deviate from the majority. Considering the privacy concerns in distributed scenarios, federated graph-level anomaly detection (FedGLAD) has emerged as a promising solution to enable collaborative detection without sharing raw data. However, existing methods suffer from poor generalization due to the reliance on unrealistic synthetic anomalies and insufficient personalization capabilities under data heterogeneity. To address these challenges, we propose a novel Federated graph-level anomaly detection approach with Cluster-adaptIve GAted Reconstruction (FedCIGAR). Specifically, we design a reconstruction-based paradigm trained on normal graphs to avoid synthetic data. Furthermore, we introduce a client-side node contribution gating mechanism and a server-side sliding window-based clustering strategy to tackle data heterogeneity. Extensive experiments demonstrate that FedCIGAR achieves superior performance and robustness in contrast to state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Investigating $Ωϕ$ Interaction and Correlation Functions

In this work, we investigate the interaction between the $Ω$ baryon and the $s\bar{s}$ meson within the framework of the quark delocalization color screening model. The spectra calculations show that no bound state is formed in any of the considered channels, while the scattering indicates that the $Ωϕ$ interaction with $J^{P}=1/2^{-}$ is weakly attractive. As for the $Ωϕ$ interactions with $J^{P}=3/2^{-}$ and $5/2^{-}$, as well as the $Ωη^{\prime}$ interaction with $J^{P}=3/2^{-}$, they are all repulsive. After an investigation on the femtoscopic correlation functions, we find that, due to the spin-averaging effect, the overall $Ωϕ$ correlation function exhibits a weak dependence on the source size, which provides a crucial significance of our model for future experimental examinations in relativistic heavy-ion collisions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Double-heavy tetraquarks with strangeness in the chiral quark model

Recently, some progresses have been made on the double-heavy tetraquarks in the experiments, such as $T_{cc}$ reported by LHCb Collaboration, and $X_{cc\bar{s}\bar{s}}$ reported by the Belle Collaboration. Coming on the heels of our previous work about $T_{cc}$ and $T_{bb}$, we present a study on the bound states and the resonant states of its companions $QQ\bar{q}\bar{s}$ ($Q=c,b; q=u, s$) tetraquarks with strange flavor in the chiral quark model. Two pictures, one with meson-meson picture, another with diquark-antidiquark picture and their couplings are considered in our calculations. Isospin violation is neglected herein. Our numerical analysis indicates that only the state $bb\bar{u}\bar{s}$ with $\frac{1}{2}(1^+)$ is bound, with the binding energy 3.5 MeV. Besides, we also find some resonant states for the double-heavy strange tetraquarks with the real scaling method.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

FedProto: Federated Prototype Learning across Heterogeneous Clients

Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space. For example, clients may differ in terms of data distribution, network latency, input/output space, and/or model architecture, which can easily lead to the misalignment of their local gradients. To improve the tolerance to heterogeneity, we propose a novel federated prototype learning (FedProto) framework in which the clients and server communicate the abstract class prototypes instead of the gradients. FedProto aggregates the local prototypes collected from different clients, and then sends the global prototypes back to all clients to regularize the training of local models. The training on each client aims to minimize the classification error on the local data while keeping the resulting local prototypes sufficiently close to the corresponding global ones. Moreover, we provide a theoretical analysis to the convergence rate of FedProto under non-convex objectives. In experiments, we propose a benchmark setting tailored for heterogeneous FL, with FedProto outperforming several recent FL approaches on multiple datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, where the IoT devices collect and share information to reflect status of the physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without external intervention over an extended period of time. The integration of IoT and ACS results in a new concept - autonomous IoT (AIoT). The sensors collect information on the system status, based on which the intelligent agents in the IoT devices as well as the Edge/Fog/Cloud servers make control decisions for the actuators to react. In order to achieve autonomy, a promising method is for the intelligent agents to leverage the techniques in the field of artificial intelligence, especially reinforcement learning (RL) and deep reinforcement learning (DRL) for decision making. In this paper, we first provide a tutorial of DRL, and then propose a general model for the applications of RL/DRL in AIoT. Next, a comprehensive survey of the state-of-art research on DRL for AIoT is presented, where the existing works are classified and summarized under the umbrella of the proposed general DRL model. Finally, the challenges and open issues for future research are identified.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Investigation of $qqqs\bar q$ pentaquarks in a chiral quark model

We investigate the pentaquark system $qqqs \bar q$ in a framework of chiral quark model. Two structures, $(qqq)(s\bar{q})$ and $(qqs)(q\bar{q})$, with all possible color, spin, flavor configurations are considered. The calculations show that there are several possible resonance states, $Sigma π$ and $N \bar{K}$ state with $IJ^P=0\frac{1}{2}^-$, $Σ^* π$ with $IJ^P=0\frac{3}{2}^-$, $Σ^* ρ$ with $IJ^P=0\frac{5}{2}^-$, $Δ\bar{K}$ with $IJ^P=1\frac{3}{2}^-$ and $Δ\bar{K}^*$ with $IJ^P=1\frac{5}{2}^-$. Where the $N \bar{K}$ state with $IJ^P=0\frac{1}{2}^-$ can be used to explain the $Λ(1405)$, and together with another state $Σπ$ is related to the two-pole structure of the scattering amplitude proposed before. The decay properties of $Λ(1520)$ prevent the assignment of $Σ^* π$ with $IJ^P=0\frac{3}{2}^-$ to $Λ(1520)$, although the energy $\sim$ 1518 MeV of $Σ^* π$ is close to experimental value of $Λ(1520)$. Other resonance states generally have a large width.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

LSTM-based Anomaly Detection for Non-linear Dynamical System

Anomaly detection for non-linear dynamical system plays an important role in ensuring the system stability. However, it is usually complex and has to be solved by large-scale simulation which requires extensive computing resources. In this paper, we propose a novel anomaly detection scheme in non-linear dynamical system based on Long Short-Term Memory (LSTM) to capture complex temporal changes of the time sequence and make multi-step predictions. Specifically, we first present the framework of LSTM-based anomaly detection in non-linear dynamical system, including data preprocessing, multi-step prediction and anomaly detection. According to the prediction requirement, two types of training modes are explored in multi-step prediction, where samples in a wall shear stress dataset are collected by an adaptive sliding window. On the basis of the multi-step prediction result, a Local Average with Adaptive Parameters (LAAP) algorithm is proposed to extract local numerical features of the time sequence and estimate the upcoming anomaly. The experimental results show that our proposed multi-step prediction method can achieve a higher prediction accuracy than traditional method in wall shear stress dataset, and the LAAP algorithm performs better than the absolute value-based method in anomaly detection task.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

$Y(4626)$ in a chiral constituent quark model

Recently, Belle Collaboration reported a new exotic state $Y(4620)$ with mass at 4625.9 MeV in the positronium annihilation process. Inspired by experiment, we study the tetraquark system $c\bar{s}s\bar{c}$ with quantum numbers $J^{P}=1^{-}$ in the framework of chiral constituent quark model with the help of Gaussian expansion method. Two structures, diquark-antidiquark and meson-meson, with all possible color and spin configurations are considered. The result shows that no bound state can be formed. To investigate the possible resonance states, the real scaling method is employed. Several resonance states with energies 4354, 4408, 4469, 4497 and 4531 MeV, are proposed. Taking into account the errors in calculating the $q\bar{q}$ mesons, the system errors in the calculation of four-quark system are around 60$\sim 100$ MeV. The resonance with energy 4531 MeV is possible the candidate of the newly found state $Y(4620)$.

0 citations0 reviewsNo code linkedOpen access