arXiv subjects

Jun Zhuang

Publications and source records attributed to Jun Zhuang.

At least 37 records · Page 2Linked to original sources

arXiv2024

RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling

Node classification using Graph Neural Networks (GNNs) has been widely applied in various practical scenarios, such as predicting user interests and detecting communities in social networks. However, recent studies have shown that graph-structured networks often contain potential noise and attacks, in the form of topological perturbations and weight disturbances, which can lead to decreased classification performance in GNNs. To improve the robustness of the model, we propose a novel method: Random Walk Negative Sampling Graph Convolutional Network (RW-NSGCN). Specifically, RW-NSGCN integrates the Random Walk with Restart (RWR) and PageRank (PGR) algorithms for negative sampling and employs a Determinantal Point Process (DPP)-based GCN for convolution operations. RWR leverages both global and local information to manage noise and local variations, while PGR assesses node importance to stabilize the topological structure. The DPP-based GCN ensures diversity among negative samples and aggregates their features to produce robust node embeddings, thereby improving classification performance. Experimental results demonstrate that the RW-NSGCN model effectively addresses network topology attacks and weight instability, increasing the accuracy of anomaly detection and overall stability. In terms of classification accuracy, RW-NSGCN significantly outperforms existing methods, showing greater resilience across various scenarios and effectively mitigating the impact of such vulnerabilities.

cs.LG↗

arXiv2024

Probing the interaction energy of two $^{85}$Rb atoms in an optical tweezer via spin-motion coupling

The inherent polarization gradients in tight optical tweezers can be used to couple the atomic spins to the two-body motion under the action of a microwave spin-flip transition, so that such a spin-motion coupling offers an important control knob on the motional states of optically trapped two colliding atoms. Here, after preparing two elastically scattering $^{85}$Rb atoms in the three-dimensional ground-state in the optical tweezer, we employed this control in order to probe the colliding energies of elastic and inelastic channels. The combination of microwave spectra and corresponding s-wave pseudopotential model allows us to infer the effect of the state-dependent trapping potentials on the elastic colliding energies, as well as to reveal how the presence of inelastic interactions affects elastic part of the relative potential. Our work shows that the spin-motion coupling in a tight optical tweezer expand the experimental toolbox for fundamental studies of ultracold collisions in the two body systems with reactive collisions, and potentially for that of more complex interactions, such as optically trapped atom-molecule and molecule-molecule interactions.

physics.atom-ph↗

arXiv2024

Enhancing the Resilience of Graph Neural Networks to Topological Perturbations in Sparse Graphs

Graph neural networks (GNNs) have been extensively employed in node classification. Nevertheless, recent studies indicate that GNNs are vulnerable to topological perturbations, such as adversarial attacks and edge disruptions. Considerable efforts have been devoted to mitigating these challenges. For example, pioneering Bayesian methodologies, including GraphSS and LlnDT, incorporate Bayesian label transitions and topology-based label sampling to strengthen the robustness of GNNs. However, GraphSS is hindered by slow convergence, while LlnDT faces challenges in sparse graphs. To overcome these limitations, we propose a novel label inference framework, TraTopo, which combines topology-driven label propagation, Bayesian label transitions, and link analysis via random walks. TraTopo significantly surpasses its predecessors on sparse graphs by utilizing random walk sampling, specifically targeting isolated nodes for link prediction, thus enhancing its effectiveness in topological sampling contexts. Additionally, TraTopo employs a shortest-path strategy to refine link prediction, thereby reducing predictive overhead and improving label inference accuracy. Empirical evaluations highlight TraTopo's superiority in node classification, significantly exceeding contemporary GCN models in accuracy.

cs.LG↗

arXiv2024

Debiasing Machine Unlearning with Counterfactual Examples

The right to be forgotten (RTBF) seeks to safeguard individuals from the enduring effects of their historical actions by implementing machine-learning techniques. These techniques facilitate the deletion of previously acquired knowledge without requiring extensive model retraining. However, they often overlook a critical issue: unlearning processes bias. This bias emerges from two main sources: (1) data-level bias, characterized by uneven data removal, and (2) algorithm-level bias, which leads to the contamination of the remaining dataset, thereby degrading model accuracy. In this work, we analyze the causal factors behind the unlearning process and mitigate biases at both data and algorithmic levels. Typically, we introduce an intervention-based approach, where knowledge to forget is erased with a debiased dataset. Besides, we guide the forgetting procedure by leveraging counterfactual examples, as they maintain semantic data consistency without hurting performance on the remaining dataset. Experimental results demonstrate that our method outperforms existing machine unlearning baselines on evaluation metrics.

cs.LG↗

arXiv2024

Mitigating noise of residual electric fields for single Rydberg atoms with electron photodesorption

Rydberg atoms as versatile tools for quantum applications are extremely sensitive to electric fields. When utilizing these atoms, it becomes imperative to comprehensively characterize and mitigate any residual electric fields present in the environment. Particularly for single Rydberg atoms trapped in optical tweezers in a compact quartz vacuum cell, we have identified that a significant source of background electric fields originates from electrons bound to the cell surface. These electrons are generated by the 297-nm light used for single-photon Rydberg excitation. Furthermore, once the electrons are desorbed from the surface through exposure to ultraviolet light, the incoherent ground-Rydberg transition undergoes a transformation into coherent excitation, since the noise of residual electric fields are effectively mitigated. Our studies promote enhanced control and reliable performance of Rydberg atom-based systems, thereby paving the way for advancements in quantum information processing, the realization of high-fidelity quantum gates, and the development of precise quantum sensors.

physics.atom-ph↗

arXiv2024

Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning

As new research on Large Language Models (LLMs) continues, it is difficult to keep up with new research and models. To help researchers synthesize the new research many have written survey papers, but even those have become numerous. In this paper, we develop a method to automatically assign survey papers to a taxonomy. We collect the metadata of 144 LLM survey papers and explore three paradigms to classify papers within the taxonomy. Our work indicates that leveraging graph structure information on co-category graphs can significantly outperform the language models in two paradigms; pre-trained language models' fine-tuning and zero-shot/few-shot classifications using LLMs. We find that our model surpasses an average human recognition level and that fine-tuning LLMs using weak labels generated by a smaller model, such as the GCN in this study, can be more effective than using ground-truth labels, revealing the potential of weak-to-strong generalization in the taxonomy classification task.

cs.CL↗

arXiv2023

Robust Node Representation Learning via Graph Variational Diffusion Networks

Node representation learning by using Graph Neural Networks (GNNs) has been widely explored. However, in recent years, compelling evidence has revealed that GNN-based node representation learning can be substantially deteriorated by delicately-crafted perturbations in a graph structure. To learn robust node representation in the presence of perturbations, various works have been proposed to safeguard GNNs. Within these existing works, Bayesian label transition has been proven to be more effective, but this method is extensively reliant on a well-built prior distribution. The variational inference could address this limitation by sampling the latent node embedding from a Gaussian prior distribution. Besides, leveraging the Gaussian distribution (noise) in hidden layers is an appealing strategy to strengthen the robustness of GNNs. However, our experiments indicate that such a strategy can cause over-smoothing issues during node aggregation. In this work, we propose the Graph Variational Diffusion Network (GVDN), a new node encoder that effectively manipulates Gaussian noise to safeguard robustness on perturbed graphs while alleviating over-smoothing issues through two mechanisms: Gaussian diffusion and node embedding propagation. Thanks to these two mechanisms, our model can generate robust node embeddings for recovery. Specifically, we design a retraining mechanism using the generated node embedding to recover the performance of node classifications in the presence of perturbations. The experiments verify the effectiveness of our proposed model across six public datasets.

cs.LG↗

arXiv2022

Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation

Node classification using Graph Neural Networks (GNNs) has been widely applied in various real-world scenarios. However, in recent years, compelling evidence emerges that the performance of GNN-based node classification may deteriorate substantially by topological perturbation, such as random connections or adversarial attacks. Various solutions, such as topological denoising methods and mechanism design methods, have been proposed to develop robust GNN-based node classifiers but none of these works can fully address the problems related to topological perturbations. Recently, the Bayesian label transition model is proposed to tackle this issue but its slow convergence may lead to inferior performance. In this work, we propose a new label inference model, namely LInDT, which integrates both Bayesian label transition and topology-based label propagation for improving the robustness of GNNs against topological perturbations. LInDT is superior to existing label transition methods as it improves the label prediction of uncertain nodes by utilizing neighborhood-based label propagation leading to better convergence of label inference. Besides, LIndT adopts asymmetric Dirichlet distribution as a prior, which also helps it to improve label inference. Extensive experiments on five graph datasets demonstrate the superiority of LInDT for GNN-based node classification under three scenarios of topological perturbations.

cs.LG↗

arXiv2022

Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision

In recent years, plentiful evidence illustrates that Graph Convolutional Networks (GCNs) achieve extraordinary accomplishments on the node classification task. However, GCNs may be vulnerable to adversarial attacks on label-scarce dynamic graphs. Many existing works aim to strengthen the robustness of GCNs; for instance, adversarial training is used to shield GCNs against malicious perturbations. However, these works fail on dynamic graphs for which label scarcity is a pressing issue. To overcome label scarcity, self-training attempts to iteratively assign pseudo-labels to highly confident unlabeled nodes but such attempts may suffer serious degradation under dynamic graph perturbations. In this paper, we generalize noisy supervision as a kind of self-supervised learning method and then propose a novel Bayesian self-supervision model, namely GraphSS, to address the issue. Extensive experiments demonstrate that GraphSS can not only affirmatively alert the perturbations on dynamic graphs but also effectively recover the prediction of a node classifier when the graph is under such perturbations. These two advantages prove to be generalized over three classic GCNs across five public graph datasets.

cs.LG↗

arXiv2022

Deperturbation of Online Social Networks via Bayesian Label Transition

Online social networks (OSNs) classify users into different categories based on their online activities and interests, a task which is referred as a node classification task. Such a task can be solved effectively using Graph Convolutional Networks (GCNs). However, a small number of users, so-called perturbators, may perform random activities on an OSN, which significantly deteriorate the performance of a GCN-based node classification task. Existing works in this direction defend GCNs either by adversarial training or by identifying the attacker nodes followed by their removal. However, both of these approaches require that the attack patterns or attacker nodes be identified first, which is difficult in the scenario when the number of perturbator nodes is very small. In this work, we develop a GCN defense model, namely GraphLT, which uses the concept of label transition. GraphLT assumes that perturbators' random activities deteriorate GCN's performance. To overcome this issue, GraphLT subsequently uses a novel Bayesian label transition model, which takes GCN's predicted labels and applies label transitions by Gibbs-sampling-based inference and thus repairs GCN's prediction to achieve better node classification. Extensive experiments on seven benchmark datasets show that GraphLT considerably enhances the performance of the node classifier in an unperturbed environment; furthermore, it validates that GraphLT can successfully repair a GCN-based node classifier with superior performance than several competing methods.

cs.LG↗

arXiv2021

Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks

Supervised learning, while deployed in real-life scenarios, often encounters instances of unknown classes. Conventional algorithms for training a supervised learning model do not provide an option to detect such instances, so they miss-classify such instances with 100% probability. Open Set Recognition (OSR) and Non-Exhaustive Learning (NEL) are potential solutions to overcome this problem. Most existing methods of OSR first classify members of existing classes and then identify instances of new classes. However, many of the existing methods of OSR only makes a binary decision, i.e., they only identify the existence of the unknown class. Hence, such methods cannot distinguish test instances belonging to incremental unseen classes. On the other hand, the majority of NEL methods often make a parametric assumption over the data distribution, which either fail to return good results, due to the reason that real-life complex datasets may not follow a well-known data distribution. In this paper, we propose a new online non-exhaustive learning model, namely, Non-Exhaustive Gaussian Mixture Generative Adversarial Networks (NE-GM-GAN) to address these issues. Our proposed model synthesizes Gaussian mixture based latent representation over a deep generative model, such as GAN, for incremental detection of instances of emerging classes in the test data. Extensive experimental results on several benchmark datasets show that NE-GM-GAN significantly outperforms the state-of-the-art methods in detecting instances of novel classes in streaming data.

cs.LG↗

arXiv2021

Defect-free arbitrary-geometry assembly of mixed-species atom arrays

Optically trapped mixed-species single atom arrays with arbitrary geometries are an attractive and promising platform for various applications, because tunable quantum systems with multiple components provide extra degrees of freedom for experimental control. Here, we report the first demonstration of two-dimensional $6\times4$ dual-species atom assembly with a filling fraction of 0.88 (0.89) for $^{85}$Rb ($^{87}$Rb) atoms. This mixed-species atomic synthetic is achieved via rearranging initially randomly distributed atoms using a sorting algorithm (heuristic heteronuclear algorithm) which is proposed for bottom-up atom assembly with both user-defined geometries and two-species atom number ratios. Our fully tunable hybrid-atom system of scalable advantages is a good starting point for high-fidelity quantum logic, many-body quantum simulation and forming defect-free single molecule arrays.

physics.atom-ph↗

arXiv2021

Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks

Microscopic images from multiple modalities can produce plentiful experimental information. In practice, biological or physical constraints under a given observation period may prevent researchers from acquiring enough microscopic scanning. Recent studies demonstrate that image synthesis is one of the popular approaches to release such constraints. Nonetheless, most existing synthesis approaches only translate images from the source domain to the target domain without solid geometric associations. To embrace this challenge, we propose an innovative model architecture, BANIS, to synthesize diversified microscopic images from multi-source domains with distinct geometric features. The experimental outcomes indicate that BANIS successfully synthesizes favorable image pairs on C. elegans microscopy embryonic images. To the best of our knowledge, BANIS is the first application to synthesize microscopic images that associate distinct spatial geometric features from multi-source domains.

eess.IV↗

arXiv2021

Photovoltaic Self-Powered Gas Sensing: A Review

The self-powered sensing system could harness ambient energy to power the sensor without the need for external electrical energy. Recently, the concept of photovoltaic (PV) self-powered gas sensing has aroused wider attentions due to room-temperature operation, low power consumption, small size and potential applications. The PV self-powered gas sensors integrate the photovoltaic effects and the gas sensing function into a single chip, which could truly achieve the goal of zero power consumption for an independent gas sensing device. As an emerging concept, the PV self-powered gas sensing has been achieved by using different strategies, including integrated gas sensor and solar cell, integrated light filter and solar cell, gas-sensitive heterojunction photovoltaics, and gas-sensitive lateral photovoltaics, respectively. The purpose of this review is to summarize recent advances of PV self-powered gas sensing and also remark on the directions for future research in this topic.

physics.app-ph↗

arXiv2020

Efficient preparation of 2D defect-free atom arrays with near-fewest sorting-atom moves

Sorting atoms stochastically loaded in optical tweezer arrays via an auxiliary mobile tweezer is an efficient approach to preparing intermediate-scale defect-free atom arrays in arbitrary geometries. However, high filling fraction of atom-by-atom assemblers is impeded by redundant sorting moves with imperfect atom transport, especially for scaling the system size to larger atom numbers. Here, we propose a new sorting algorithm (heuristic cluster algorithm, HCA) which provides near-fewest moves in our tailored atom assembler scheme and experimentally demonstrate a $5\times6$ defect-free atom array with 98.4(7)$\%$ filling fraction for one rearrangement cycle. The feature of HCA that the number of moves $N_{m}\approx N$ ($N$ is the number of defect sites to be filled) makes the filling fraction uniform as the size of atom assembler enlarged. Our method is essential to scale hundreds of assembled atoms for bottom-up quantum computation, quantum simulation and precision measurement.

quant-ph↗

arXiv2019

Preparation of a Heteronuclear Two-atom System in the 3D Motional Ground State in an Optical Tweezer

We report the realization of a heteronuclear two-atom of $^{87}$Rb-$^{85}$Rb in the ground state of an optical tweezer (OT). Starting by trapping two different isotopic single atoms, a $^{87}$Rb and a $^{85}$Rb in two strongly focused and linearly polarized OT with 4 $μ$m apart, we perform simultaneously three dimensional Raman sideband cooling for both atoms and the obtained 3D ground state probabilities of $^{87}$Rb and $^{85}$Rb are 0.91(5) and 0.91(10) respectively. There is no obvious crosstalk observed during the cooling process. We then merge them into one tweezer via a species-dependent transport, where the species-dependent potentials are made by changing the polarization of the OTs for each species from linear polarization to the desired circular polarization. The measurable increment of vibrational quantum due to merging is $0.013(1)$ for the axial dimension. This two-atom system can be used to investigate cold collisional physics, to form quantum logic gates, and to build a single heteronuclear molecule. It can also be scaled up to few-atom regime and extended to other atomic species and molecules, and thus to ultracold chemistry.

physics.atom-ph↗

arXiv2019

Revealing Energy Dependence of Quantum Defects via Two Heteronuclear Atoms in an Optical Tweezer

As a physically motivated and computationally simple model for cold atomic and molecular collisions, the multichannel quantum defect theory (MQDT) with frame transformation (FT) formalism provides an analytical treatment of scattering resonances in an arbitrary partial wave between alkali-metal atoms, leading to the experimental observation of $p-$ and $d-$wave resonances. However, the inconsistency of quantum defects for describing scattering resonances shows up when compared with experiments. Here, with two heteronuclear atoms in the ground state of an optical tweezer, the energy dependence of quantum defects is obviously revealed by comparing the measured s-wave scattering length with the prediction of MQDT-FT. By dividing the quantum defects into energy sensitive and insensitive categories, the inconsistency is ultimately removed while retaining the analytic structure of MQDT-FT. This study represents a significant improvement in the analytical MQDT-FT and demonstrates that a clean two-particle system is valuable to the test of collisional physics.

quant-ph↗

arXiv2013

If graphynes turn into graphene: the thermal stability study

The thermal stability of $α$-, $β$-, 6,6,12-graphyne and graphdiyne was studied by a statistic model, which was seriously tested by classical molecular dynamics simulations. By first-principles calculations of related potential energy curves, the model predicts that all the lifetime of free-standing single layer graphynes considered is more than 10$^{44}$ years at room temperature. When the temperature gets up to 1000 K, they are still very stable, but quickly turn into graphene if the temperature is about 2000 K

cond-mat.mes-hall↗