arXiv subjects

Quan Zhang

Publications and source records attributed to Quan Zhang.

At least 55 records · Page 3Linked to original sources

arXiv2024

Development of a Simple and Novel Digital Twin Framework for Industrial Robots in Intelligent robotics manufacturing

This paper has proposed an easily replicable and novel approach for developing a Digital Twin (DT) system for industrial robots in intelligent manufacturing applications. Our framework enables effective communication via Robot Web Service (RWS), while a real-time simulation is implemented in Unity 3D and Web-based Platform without any other 3rd party tools. The framework can do real-time visualization and control of the entire work process, as well as implement real-time path planning based on algorithms executed in MATLAB. Results verify the high communication efficiency with a refresh rate of only $17 ms$. Furthermore, our developed web-based platform and Graphical User Interface (GUI) enable easy accessibility and user-friendliness in real-time control.

cs.RO↗

arXiv2024

Quantum-Classical Machine learning by Hybrid Tensor Networks

Tensor networks (TN) have found a wide use in machine learning, and in particular, TN and deep learning bear striking similarities. In this work, we propose the quantum-classical hybrid tensor networks (HTN) which combine tensor networks with classical neural networks in a uniform deep learning framework to overcome the limitations of regular tensor networks in machine learning. We first analyze the limitations of regular tensor networks in the applications of machine learning involving the representation power and architecture scalability. We conclude that in fact the regular tensor networks are not competent to be the basic building blocks of deep learning. Then, we discuss the performance of HTN which overcome all the deficiency of regular tensor networks for machine learning. In this sense, we are able to train HTN in the deep learning way which is the standard combination of algorithms such as Back Propagation and Stochastic Gradient Descent. We finally provide two applicable cases to show the potential applications of HTN, including quantum states classification and quantum-classical autoencoder. These cases also demonstrate the great potentiality to design various HTN in deep learning way.

cs.LG↗

arXiv2024

High-density single-atom electrocatalytic centers on two-dimensional topological platinum tellurides with Te-vacancy superstructure

Chemical activation of the intrinsically inert basal planes of transition metal dichalcogenides (TMDs) is crucial for developing high-efficiency electrocatalysts for energy technology applications. Here we report the discovery of an efficient TMD-based topological catalyst for hydrogen evolution reaction (HER), containing high-density single-atom reactive centers on a few-layer (7x7)-PtTe2-x superstructure with a Te-vacancy density of x. Compared with pristine Pt(111), PtTe2, and (2x2)-PtTe2-x, (7x7)-PtTe2-x exhibits superior HER performance owing to its substantially increased density of undercoordinated Pt sites, and displays exceptional catalytic stability when operating at high current densities. Our first-principles calculations confirm that multiple types of undercoordinated Pt sites on (7x7)-PtTe2-x exhibit favorable hydrogen adsorption Gibbs free energies, and that the reactive sites can further increase their population upon increasing hydrogen coverage. Both the (2x2)- and (7x7)-PtTe2-x are also shown to possess nontrivial band topology with robust edge states that may further facilitate HER.

cond-mat.mtrl-sci↗

arXiv2024

Module control of network analysis in psychopathology

The network approach to characterizing psychopathology departs from traditional latent categorical and dimensional approaches. Causal interplay among symptoms contributed to dynamic psychopathology system. Therefore, analyzing the symptom clusters is critical for understanding mental disorders. Furthermore, despite extensive research studying the topological features of symptom networks, the control relationships between symptoms remain largely unclear. Here, we present a novel systematizing concept, module control, to analyze the control principle of the symptom network at a module level. We introduce Module Control Network (MCN) to identify key modules that regulate the network's behavior. By applying our approach to a multivariate psychological dataset, we discover that non-emotional modules, such as sleep-related and stress-related modules, are the primary controlling modules in the symptom network. Our findings indicate that module control can expose central symptom cluster governing psychopathology network, offering novel insights into the underlying mechanisms of mental disorders and individualized approach to psychological interventions.

q-bio.NC↗

arXiv2024

Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map

Transmission interface power flow adjustment is a critical measure to ensure the security and economy operation of power systems. However, conventional model-based adjustment schemes are limited by the increasing variations and uncertainties occur in power systems, where the adjustment problems of different transmission interfaces are often treated as several independent tasks, ignoring their coupling relationship and even leading to conflict decisions. In this paper, we introduce a novel data-driven deep reinforcement learning (DRL) approach, to handle multiple power flow adjustment tasks jointly instead of learning each task from scratch. At the heart of the proposed method is a multi-task attribution map (MAM), which enables the DRL agent to explicitly attribute each transmission interface task to different power system nodes with task-adaptive attention weights. Based on this MAM, the agent can further provide effective strategies to solve the multi-task adjustment problem with a near-optimal operation cost. Simulation results on the IEEE 118-bus system, a realistic 300-bus system in China, and a very large European system with 9241 buses demonstrate that the proposed method significantly improves the performance compared with several baseline methods, and exhibits high interpretability with the learnable MAM.

eess.SY↗

arXiv2024

Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications

Presently, with the assistance of advanced LLM application development frameworks, more and more LLM-powered applications can effortlessly augment the LLMs' knowledge with external content using the retrieval augmented generation (RAG) technique. However, these frameworks' designs do not have sufficient consideration of the risk of external content, thereby allowing attackers to undermine the applications developed with these frameworks. In this paper, we reveal a new threat to LLM-powered applications, termed retrieval poisoning, where attackers can guide the application to yield malicious responses during the RAG process. Specifically, through the analysis of LLM application frameworks, attackers can craft documents visually indistinguishable from benign ones. Despite the documents providing correct information, once they are used as reference sources for RAG, the application is misled into generating incorrect responses. Our preliminary experiments indicate that attackers can mislead LLMs with an 88.33\% success rate, and achieve a 66.67\% success rate in the real-world application, demonstrating the potential impact of retrieval poisoning.

cs.CR↗

arXiv2024

When Fuzzing Meets LLMs: Challenges and Opportunities

Fuzzing, a widely-used technique for bug detection, has seen advancements through Large Language Models (LLMs). Despite their potential, LLMs face specific challenges in fuzzing. In this paper, we identified five major challenges of LLM-assisted fuzzing. To support our findings, we revisited the most recent papers from top-tier conferences, confirming that these challenges are widespread. As a remedy, we propose some actionable recommendations to help improve applying LLM in Fuzzing and conduct preliminary evaluations on DBMS fuzzing. The results demonstrate that our recommendations effectively address the identified challenges.

cs.SE↗

arXiv2024

MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection

Multi-modal 3D object detection models for automated driving have demonstrated exceptional performance on computer vision benchmarks like nuScenes. However, their reliance on densely sampled LiDAR point clouds and meticulously calibrated sensor arrays poses challenges for real-world applications. Issues such as sensor misalignment, miscalibration, and disparate sampling frequencies lead to spatial and temporal misalignment in data from LiDAR and cameras. Additionally, the integrity of LiDAR and camera data is often compromised by adverse environmental conditions such as inclement weather, leading to occlusions and noise interference. To address this challenge, we introduce MultiCorrupt, a comprehensive benchmark designed to evaluate the robustness of multi-modal 3D object detectors against ten distinct types of corruptions. We evaluate five state-of-the-art multi-modal detectors on MultiCorrupt and analyze their performance in terms of their resistance ability. Our results show that existing methods exhibit varying degrees of robustness depending on the type of corruption and their fusion strategy. We provide insights into which multi-modal design choices make such models robust against certain perturbations. The dataset generation code and benchmark are open-sourced at https://github.com/ika-rwth-aachen/MultiCorrupt.

cs.CV↗

arXiv2024

Distilling Semantic Priors from SAM to Efficient Image Restoration Models

In image restoration (IR), leveraging semantic priors from segmentation models has been a common approach to improve performance. The recent segment anything model (SAM) has emerged as a powerful tool for extracting advanced semantic priors to enhance IR tasks. However, the computational cost of SAM is prohibitive for IR, compared to existing smaller IR models. The incorporation of SAM for extracting semantic priors considerably hampers the model inference efficiency. To address this issue, we propose a general framework to distill SAM's semantic knowledge to boost exiting IR models without interfering with their inference process. Specifically, our proposed framework consists of the semantic priors fusion (SPF) scheme and the semantic priors distillation (SPD) scheme. SPF fuses two kinds of information between the restored image predicted by the original IR model and the semantic mask predicted by SAM for the refined restored image. SPD leverages a self-distillation manner to distill the fused semantic priors to boost the performance of original IR models. Additionally, we design a semantic-guided relation (SGR) module for SPD, which ensures semantic feature representation space consistency to fully distill the priors. We demonstrate the effectiveness of our framework across multiple IR models and tasks, including deraining, deblurring, and denoising.

cs.CV↗

arXiv2024

View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network

Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. However, as a more practical scenario, aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID, the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features, namely hierarchical subtractive separation and orthogonal loss, where the former separates these two features inside the VDT, and the latter constrains these two to be independent. In addition, we contribute a large-scale AGPReID dataset called CARGO, consisting of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID, keeping the same magnitude of computational complexity. Our project is available at https://github.com/LinlyAC/VDT-AGPReID

cs.CV↗

arXiv2024

Room-temperature intrinsic ferromagnetism of two-dimensional Na2Cl crystals originated by s- and p-orbitals

Ferromagnetism, as one of the most valuable properties of materials, has attracted sustained and widespread interest in basic and applied research from ancient compasses to modern electronic devices. Traditionally, intrinsic ferromagnetism has been attributed to the permanent magnetic moment induced by partially filled d- or f-orbitals. However, the development of ferromagnetic materials has been limited by this electronic structure convention. Thus, the identification of additional materials that are not constrained by this conventional rule but also exhibit intrinsic ferromagnetism is highly expected and may impact all the fields based on ferromagnetism. Here, we report the direct observation of room-temperature intrinsic ferromagnetism in two-dimensional (2D) Na2Cl crystals, in which there are only partially filled s- and p-orbitals rather than d- or f-orbitals, using the superconducting quantum interference device (SQUID) and magnetic force microscope (MFM). These Na2Cl crystals formed in reduced graphene oxide (rGO) membranes have an unconventional stoichiometric structure leading to unique electron and spin distributions. And the structure of these 2D Na2Cl crystals, including the Na and Cl sites, is characterized in situ for the first time and directly observed by cryo-electron microscopy (cryo-EM) based on the observed difference in contrast between Na stacked with Cl and single Na. These findings break the conventional rule of intrinsic ferromagnetism and provide new insights into the design of novel magnetic and electronic devices and transistors with a size down to the atomic scale.

cond-mat.mtrl-sci↗

arXiv2023

Numerical study on the effects of fluid properties in electrohydrodynamic pulsating jet under constant voltage

Pulsating jet is one of the common working modes in electrohydrodynamic printing (EHDP) that process is highly affected by operating parameters and material properties. In this paper, the processes of pulsating jet for liquids with different physical properties were investigated using numerical simulation. An electrohydrodynamic solver was established, and a charge flux restricting step was adopted to ensure a realistic distribution of free charges. Three various ejection regimes were observed in our simulations: oscillating cone (OC), choked jet (CJ), and stable cone-jet (SJ). We found that three dimensionless numbers relating to liquid properties determined the ejection regime: the Ohnesorge number, Q0εr/Q, and Q0/(QRe). Based on those dimensionless numbers, the roles of liquid properties on pulsating jet (OC and CJ) were analyzed. In OC, the break of the jet is due to the significant oscillation of the Taylor cone, which is mainly affected by viscosity and conductivity. In CJ, the jet emission is terminated by the excessive resistant force in the cone-jet transition region. For liquids with low and medium viscosity, the dominant resistant force is the polarization force or viscous force when εrRe is larger or smaller than 1, respectively. For high viscosity liquids, the viscous force always becomes the major resistance. These results further reveal the physical mechanism of pulsating jet and can be used to guide its application.

physics.flu-dyn↗

arXiv2023

Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-varying Covariates

We propose Bayesian nonparametric Weibull delegate racing (WDR) for survival analysis with competing events and achieve both model interpretability and flexibility. Utilizing a natural mechanism of surviving competing events, we assume a race among a potentially infinite number of sub-events. In doing this, WDR accommodates nonlinear covariate effects with no need of data transformation. Moreover, WDR is able to handle left truncation, time-varying covariates, different types of censoring, and missing event times or types. We develop an efficient MCMC algorithm based on Gibbs sampling for Bayesian inference and provide an \texttt{R} package. Synthetic data analysis and comparison with benchmark approaches demonstrate WDR's outstanding performance and parsimonious nonlinear modeling capacity. In addition, we analyze two real data sets and showcase advantages of WDR. Specifically, we study time to death of three types of lymphoma and show the potential of WDR in modeling nonlinear covariate effects and discovering new diseases. We also use WDR to investigate the age at onset of mild cognitive impairment and interpret the accelerating or decelerating effects of biomarkers on the progression of Alzheimer's disease.

stat.ME↗

arXiv2023

HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks

Hypergraph neural networks (HGNN) have shown superior performance in various deep learning tasks, leveraging the high-order representation ability to formulate complex correlations among data by connecting two or more nodes through hyperedge modeling. Despite the well-studied adversarial attacks on Graph Neural Networks (GNN), there is few study on adversarial attacks against HGNN, which leads to a threat to the safety of HGNN applications. In this paper, we introduce HyperAttack, the first white-box adversarial attack framework against hypergraph neural networks. HyperAttack conducts a white-box structure attack by perturbing hyperedge link status towards the target node with the guidance of both gradients and integrated gradients. We evaluate HyperAttack on the widely-used Cora and PubMed datasets and three hypergraph neural networks with typical hypergraph modeling techniques. Compared to state-of-the-art white-box structural attack methods for GNN, HyperAttack achieves a 10-20X improvement in time efficiency while also increasing attack success rates by 1.3%-3.7%. The results show that HyperAttack can achieve efficient adversarial attacks that balance effectiveness and time costs.

cs.LG↗

arXiv2023

Deformation insensitive thermal conductance of the designed Si metamaterial

The thermal management have been widely focused due to broad applications. Generally, the deformation can largely tune the thermal transport. The main challenge of flexible electronics/ materials is to maintain thermal conductance under large deformation. This work investigates the thermal conductance of a nano-designed Si metamaterial constructed with curved nanobeams by molecular dynamics simulation. Interestingly, it shows that the thermal conductance of the nano-designed Si metamaterial is insensitive under a large deformation (strain~-41%). The new feature comes from the designed curved nanobeams which makes a quasi-zero stiffness. Further calculations show that, when under a large deformation, the average stress in nanobeam is ultra-small (<151 MPa) and its phonon density of states are little changed. This work provides valuable insights on multifunction, such as both stable thermal and mechanical properties, of nano-designed metamaterials.

physics.app-ph↗

arXiv2023

Optimization for Amortized Inverse Problems

Incorporating a deep generative model as the prior distribution in inverse problems has established substantial success in reconstructing images from corrupted observations. Notwithstanding, the existing optimization approaches use gradient descent largely without adapting to the non-convex nature of the problem and can be sensitive to initial values, impeding further performance improvement. In this paper, we propose an efficient amortized optimization scheme for inverse problems with a deep generative prior. Specifically, the optimization task with high degrees of difficulty is decomposed into optimizing a sequence of much easier ones. We provide a theoretical guarantee of the proposed algorithm and empirically validate it on different inverse problems. As a result, our approach outperforms baseline methods qualitatively and quantitatively by a large margin.

cs.LG↗

arXiv2022

MCMC-Interactive Variational Inference

Leveraging well-established MCMC strategies, we propose MCMC-interactive variational inference (MIVI) to not only estimate the posterior in a time constrained manner, but also facilitate the design of MCMC transitions. Constructing a variational distribution followed by a short Markov chain that has parameters to learn, MIVI takes advantage of the complementary properties of variational inference and MCMC to encourage mutual improvement. On one hand, with the variational distribution locating high posterior density regions, the Markov chain is optimized within the variational inference framework to efficiently target the posterior despite a small number of transitions. On the other hand, the optimized Markov chain with considerable flexibility guides the variational distribution towards the posterior and alleviates its underestimation of uncertainty. Furthermore, we prove the optimized Markov chain in MIVI admits extrapolation, which means its marginal distribution gets closer to the true posterior as the chain grows. Therefore, the Markov chain can be used separately as an efficient MCMC scheme. Experiments show that MIVI not only accurately and efficiently approximates the posteriors but also facilitates designs of stochastic gradient MCMC and Gibbs sampling transitions.

cs.LG↗

arXiv2022

On the design of Massive MIMO-QAM detector via $\ell_2$-Box ADMM approach

In this letter, we develop an $\ell_2$-box maximum likelihood (ML) formulation for massive multiple-input multiple-output (MIMO) quadrature amplitude modulation (QAM) signal detection and customize an alternating direction method of multipliers (ADMM) algorithm to solve the nonconvex optimization model. In the $\ell_2$-box ADMM implementation, all variables are solved analytically. Moreover, several theoretical results related to convergence, iteration complexity, and computational complexity are presented. Simulation results demonstrate the effectiveness of the proposed $\ell_2$-box ADMM detector in comparison with state-of-the-arts approaches.

cs.IT↗