arXiv subjects

Dong Li

Publications and source records attributed to Dong Li.

At least 217 records · Page 12Linked to original sources

arXiv2025

Partial Convolution Meets Visual Attention

Designing an efficient and effective neural network has remained a prominent topic in computer vision research. Depthwise onvolution (DWConv) is widely used in efficient CNNs or ViTs, but it needs frequent memory access during inference, which leads to low throughput. FasterNet attempts to introduce partial convolution (PConv) as an alternative to DWConv but compromises the accuracy due to underutilized channels. To remedy this shortcoming and consider the redundancy between feature map channels, we introduce a novel Partial visual ATtention mechanism (PAT) that can efficiently combine PConv with visual attention. Our exploration indicates that the partial attention mechanism can completely replace the full attention mechanism and reduce model parameters and FLOPs. Our PAT can derive three types of blocks: Partial Channel-Attention block (PAT_ch), Partial Spatial-Attention block (PAT_sp) and Partial Self-Attention block (PAT_sf). First, PAT_ch integrates the enhanced Gaussian channel attention mechanism to infuse global distribution information into the untouched channels of PConv. Second, we introduce the spatial-wise attention to the MLP layer to further improve model accuracy. Finally, we replace PAT_ch in the last stage with the self-attention mechanism to extend the global receptive field. Building upon PAT, we propose a novel hybrid network family, named PATNet, which achieves superior top-1 accuracy and inference speed compared to FasterNet on ImageNet-1K classification and excel in both detection and segmentation on the COCO dataset. Particularly, our PATNet-T2 achieves 1.3% higher accuracy than FasterNet-T2, while exhibiting 25% higher GPU throughput and 24% lower CPU latency.

cs.CV↗

arXiv2025

Exploiting Hybrid Active and Passive Multiple Access via Slotted ALOHA-Driven Backscatter Communications

In conventional backscatter communication (BackCom) systems, time division multiple access (TDMA) and frequency division multiple access (FDMA) are generally adopted for multiuser backscattering due to their simplicity in implementation. However, as the number of backscatter devices (BDs) proliferates, there will be a high overhead under the traditional centralized control techniques, and the inter-user coordination is unaffordable for the passive BDs, which are of scarce concern in existing works and remain unsolved. To this end, in this paper, we propose a slotted ALOHA-based random access for BackCom systems, in which each BD is randomly chosen and is allowed to coexist with one active device for hybrid multiple access. To excavate and evaluate the performance, a resource allocation problem for max-min transmission rate is formulated, where transmit antenna selection, receive beamforming design, reflection coefficient adjustment, power control, and access probability determination are jointly considered. To deal with this intractable problem, we first transform the objective function with the max-min form into an equivalent linear one, and then decompose the resulting problem into three sub-problems. Next, a block coordinate descent (BCD)-based greedy algorithm with a penalty function, successive convex approximation, and linear programming are designed to obtain sub-optimal solutions for tractable analysis. Simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms in terms of transmission rate and fairness.

cs.IT↗

arXiv2025

Flat bands and temperature-driven phase transition in quasi-one-dimensional zigzag chains

Flat-band materials have garnered extensive attention due to their captivating properties associated with strong correlation effects. While flat bands have been discovered in several types of 2D materials, their existence in 1D systems remains elusive. Here, we propose a 1D frustrated lattice, specifically the 1D zigzag lattice, as a platform for hosting flat bands. This lattice can be experimentally realized by growing CuTe chains on Cu(111). The presence of flat bands was confirmed by tight-binding model analysis, first-principles calculations, and angle-resolved photoemission spectroscopy measurements. In addition, we discovered a temperature-driven phase transition at approximately 250 K. Detailed analyses demonstrate that the system has a Tomonaga-Luttinger liquid behavior, accompanied by spin-charge separation effects. Our work unveils new prospects for investigating strongly correlated electron behaviors and topological properties in the 1D limit.

cond-mat.str-el↗

arXiv2025

Extracting intrinsic superconducting properties in intercalated layered superconductors using an extended 2D Tinkham model

Bulk two dimensional (2D) superconductivity has gained considerable attention due to its intricate interplay between symmetry breaking, nontrivial topology, 2D phase fluctuations, and unconventional superconductivity. However, certain intercalated layered superconductors, despite their short c-axis superconducting coherence length, have been misclassified as anisotropic three-dimensional (3D) superconductors. Here, we investigate (Li,Fe)OHFeSe superconductors with varying degrees of interlayer misalignment, revealing sample-dependent superconducting dimensionality while consistently observing Berezinskii Kosterlitz Thouless (BKT) transitions. To resolve this discrepancy, we develop an extended 2D Tinkham model that quantitatively captures the blurring effects induced by interlayer misalignment. We further demonstrate the validity of this model in both (Li,Fe)OHFeSe and cetyltrimethyl ammonium (CTA+) intercalated (CTA)0.5SnSe2 superconductors, highlighting its broad applicability. This work provides valuable insights into bulk 2D superconductivity and establishes an extended 2D Tinkham model for quantitatively extracting intrinsic superconducting properties in intercalated layered superconductors, particularly those exhibiting significant interlayer misalignments.

cond-mat.supr-con↗

arXiv2025

A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks

Computational pathology foundation models (CPathFMs) have emerged as a powerful approach for analyzing histopathological data, leveraging self-supervised learning to extract robust feature representations from unlabeled whole-slide images. These models, categorized into uni-modal and multi-modal frameworks, have demonstrated promise in automating complex pathology tasks such as segmentation, classification, and biomarker discovery. However, the development of CPathFMs presents significant challenges, such as limited data accessibility, high variability across datasets, the necessity for domain-specific adaptation, and the lack of standardized evaluation benchmarks. This survey provides a comprehensive review of CPathFMs in computational pathology, focusing on datasets, adaptation strategies, and evaluation tasks. We analyze key techniques, such as contrastive learning and multi-modal integration, and highlight existing gaps in current research. Finally, we explore future directions from four perspectives for advancing CPathFMs. This survey serves as a valuable resource for researchers, clinicians, and AI practitioners, guiding the advancement of CPathFMs toward robust and clinically applicable AI-driven pathology solutions.

cs.CV↗

arXiv2025

Large covariance matrix estimation with factor-assisted variable clustering

This paper studies the covariance matrix estimation for high-dimensional time series within a new framework that combines low-rank factor and latent variable-specific cluster structures. The popular methods based on assuming the sparse error covariance matrix after taking out common factors may be invalid for many financial applications. Our formulation postulates a latent model-based error cluster structure after removing observable factors, which not only leads to more interpretable cluster patterns but also accounts for non-sparse cross-sectional correlations among the variable-specific residuals. Our method begins with using least-squares to estimate the factor loadings, followed by identifying the latent cluster structure by thresholding the scaled covariance difference measures of residuals. A novel ratio-based criterion is introduced to determine the threshold parameter when performing the developed clustering algorithm. We then establish the cluster recovery consistency of our method and derive the convergence rates of our proposed covariance matrix estimators under different norms. Finally, we demonstrate the superior finite sample performance of our proposal over the competing methods through both extensive simulations and a real data application on minimum variance portfolio.

stat.ME↗

arXiv2025

Localizing short-period pulsations in hard X-rays and Gamma-rays during an X9.0 flare

The feature of quasi-periodic pulsations (QPPs) is frequently observed in the light curve of solar and stellar flares. In this letter, we investigated the QPP at a shorter period in high-energy channels of HXRs and Ggamma-ray continuum during an X9.0 flare on October 3, 2024 (SOL2024-10-03T12:08). The X9.0 flare was simultaneously measured by the Hard X-ray Imager, the Konus-Wind, and the Spectrometer/Telescope for Imaging X-rays. The shorter period center at about 1 s was determined by the fast Fourier transform with a Bayesianbased Markov Chain Monte Carlo and the wavelet analysis method. The restructured images show that the HXR sources are mainly separated into two fragments, regarding as double footpoints. Moreover, the footpoints move significantly during the flare QPP. Our results suggest that the intermittent and impulsive energy releases during the powerful flare are mainly caused by the interaction of hot plasma loops that are rooted in double footpoints. Thus, the flare QPP at a shorter period of about 1 s in HXR and Gamma-ray continuum emissions can be well explained by the interacting loop model.

astro-ph.SR↗

arXiv2025

First Glimpse on Physical Layer Security in Internet of Vehicles: Transformed from Communication Interference to Sensing Interference

Integrated sensing and communication (ISAC) plays a crucial role in the Internet of Vehicles (IoV), serving as a key factor in enhancing driving safety and traffic efficiency. To address the security challenges of the confidential information transmission caused by the inherent openness nature of wireless medium, different from current physical layer security (PLS) methods, which depends on the \emph{additional communication interference} costing extra power resources, in this paper, we investigate a novel PLS solution, under which the \emph{inherent radar sensing interference} of the vehicles is utilized to secure wireless communications. To measure the performance of PLS methods in ISAC-based IoV systems, we first define an improved security performance metric called by transmission reliability and sensing accuracy based secrecy rate (TRSA\_SR), and derive closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), success ranging probability (SRP) for evaluating transmission reliability, security and sensing accuracy, respectively. Furthermore, we formulate an optimization problem to maximize the TRSA\_SR by utilizing radar sensing interference and joint design of the communication duration, transmission power and straight trajectory of the legitimate transmitter. Finally, the non-convex feature of formulated problem is solved through the problem decomposition and alternating optimization. Simulations indicate that compared with traditional PLS methods obtaining a non-positive STC, the proposed method achieves a secrecy rate of 3.92bps/Hz for different settings of noise power.

cs.IT↗

arXiv2025

GARAD-SLAM: 3D GAussian splatting for Real-time Anti Dynamic SLAM

The 3D Gaussian Splatting (3DGS)-based SLAM system has garnered widespread attention due to its excellent performance in real-time high-fidelity rendering. However, in real-world environments with dynamic objects, existing 3DGS-based SLAM systems often face mapping errors and tracking drift issues. To address these problems, we propose GARAD-SLAM, a real-time 3DGS-based SLAM system tailored for dynamic scenes. In terms of tracking, unlike traditional methods, we directly perform dynamic segmentation on Gaussians and map them back to the front-end to obtain dynamic point labels through a Gaussian pyramid network, achieving precise dynamic removal and robust tracking. For mapping, we impose rendering penalties on dynamically labeled Gaussians, which are updated through the network, to avoid irreversible erroneous removal caused by simple pruning. Our results on real-world datasets demonstrate that our method is competitive in tracking compared to baseline methods, generating fewer artifacts and higher-quality reconstructions in rendering.

cs.RO↗

arXiv2025

Exploring and Evaluating Real-world CXL: Use Cases and System Adoption

Compute eXpress Link (CXL) is emerging as a promising memory interface technology. However, its performance characteristics remain largely unclear due to the limited availability of production hardware. Key questions include: What are the use cases for the CXL memory? What are the impacts of the CXL memory on application performance? How to use the CXL memory in combination with existing memory components? In this work, we study the performance of three genuine CXL memory-expansion cards from different vendors. We characterize the basic performance of the CXL memory, study how HPC applications and large language models (LLM) can benefit from the CXL memory, and study the interplay between memory tiering and page interleaving. We also propose a novel data object-level interleaving policy to match the interleaving policy with memory access patterns. Our findings reveal the challenges and opportunities of using the CXL memory.

cs.PF↗

arXiv2025

EventSTR: A Benchmark Dataset and Baselines for Event Stream based Scene Text Recognition

Mainstream Scene Text Recognition (STR) algorithms are developed based on RGB cameras which are sensitive to challenging factors such as low illumination, motion blur, and cluttered backgrounds. In this paper, we propose to recognize the scene text using bio-inspired event cameras by collecting and annotating a large-scale benchmark dataset, termed EventSTR. It contains 9,928 high-definition (1280 * 720) event samples and involves both Chinese and English characters. We also benchmark multiple STR algorithms as the baselines for future works to compare. In addition, we propose a new event-based scene text recognition framework, termed SimC-ESTR. It first extracts the event features using a visual encoder and projects them into tokens using a Q-former module. More importantly, we propose to augment the vision tokens based on a memory mechanism before feeding into the large language models. A similarity-based error correction mechanism is embedded within the large language model to correct potential minor errors fundamentally based on contextual information. Extensive experiments on the newly proposed EventSTR dataset and two simulation STR datasets fully demonstrate the effectiveness of our proposed model. We believe that the dataset and algorithmic model can innovatively propose an event-based STR task and are expected to accelerate the application of event cameras in various industries. The source code and pre-trained models will be released on https://github.com/Event-AHU/EventSTR

cs.CV↗

arXiv2025

Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE

Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to predict multiple tokens, which are then verified in parallel by the larger target model. However, the limited capacity of the draft model often necessitates tree-based sampling to improve prediction accuracy, where multiple candidates are generated at each step. We identify a key limitation in this approach: the candidates at the same step are derived from the same representation, limiting diversity and reducing overall effectiveness. To address this, we propose Jakiro, leveraging Mixture of Experts (MoE), where independent experts generate diverse predictions, effectively decoupling correlations among candidates. Furthermore, we introduce a hybrid inference strategy, combining autoregressive decoding for initial tokens with parallel decoding for subsequent stages, and enhance the latter with contrastive mechanism in features to improve accuracy. Our method significantly boosts prediction accuracy and achieves higher inference speedups. Extensive experiments across diverse models validate the effectiveness and robustness of our approach, establishing a new SOTA in speculative decoding. Our codes are available at https://github.com/haiduo/Jakiro.

cs.CL↗

arXiv2025

DiffZOO: A Purely Query-Based Black-Box Attack for Red-teaming Text-to-Image Generative Model via Zeroth Order Optimization

Current text-to-image (T2I) synthesis diffusion models raise misuse concerns, particularly in creating prohibited or not-safe-for-work (NSFW) images. To address this, various safety mechanisms and red teaming attack methods are proposed to enhance or expose the T2I model's capability to generate unsuitable content. However, many red teaming attack methods assume knowledge of the text encoders, limiting their practical usage. In this work, we rethink the case of \textit{purely black-box} attacks without prior knowledge of the T2l model. To overcome the unavailability of gradients and the inability to optimize attacks within a discrete prompt space, we propose DiffZOO which applies Zeroth Order Optimization to procure gradient approximations and harnesses both C-PRV and D-PRV to enhance attack prompts within the discrete prompt domain. We evaluated our method across multiple safety mechanisms of the T2I diffusion model and online servers. Experiments on multiple state-of-the-art safety mechanisms show that DiffZOO attains an 8.5% higher average attack success rate than previous works, hence its promise as a practical red teaming tool for T2l models.

cs.CR↗

arXiv2025

Bubble Modeling and Tagging: A Stochastic Nonlinear Autoregression Approach

Economic and financial time series can feature locally explosive behavior when a bubble is formed. The economic or financial bubble, especially its dynamics, is an intriguing topic that has been attracting longstanding attention. To illustrate the dynamics of the local explosion itself, the paper presents a novel, simple, yet useful time series model, called the stochastic nonlinear autoregressive model, which is always strictly stationary and geometrically ergodic and can create long swings or persistence observed in many macroeconomic variables. When a nonlinear autoregressive coefficient is outside of a certain range, the model has periodically explosive behaviors and can then be used to portray the bubble dynamics. Further, the quasi-maximum likelihood estimation (QMLE) of our model is considered, and its strong consistency and asymptotic normality are established under minimal assumptions on innovation. A new model diagnostic checking statistic is developed for model fitting adequacy. In addition, two methods for bubble tagging are proposed, one from the residual perspective and the other from the null-state perspective. Monte Carlo simulation studies are conducted to assess the performances of the QMLE and the two bubble tagging methods in finite samples. Finally, the usefulness of the model is illustrated by an empirical application to the monthly Hang Seng Index.

math.ST↗

arXiv2025

From sparse to dense functional data in high dimensions: Revisiting phase transitions from a non-asymptotic perspective

Nonparametric estimation of the mean and covariance functions is ubiquitous in functional data analysis and local linear smoothing techniques are most frequently used. Zhang and Wang (2016) explored different types of asymptotic properties of the estimation, which reveal interesting phase transition phenomena based on the relative order of the average sampling frequency per subject $T$ to the number of subjects $n$, partitioning the data into three categories: "sparse", "semi-dense", and "ultra-dense". In an increasingly available high-dimensional scenario, where the number of functional variables $p$ is large in relation to $n$, we revisit this open problem from a non-asymptotic perspective by deriving comprehensive concentration inequalities for the local linear smoothers. Besides being of interest by themselves, our non-asymptotic results lead to elementwise maximum rates of $L_2$ convergence and uniform convergence serving as a fundamentally important tool for further convergence analysis when $p$ grows exponentially with $n$ and possibly $T$. With the presence of extra $\log p$ terms to account for the high-dimensional effect, we then investigate the scaled phase transitions and the corresponding elementwise maximum rates from sparse to semi-dense to ultra-dense functional data in high dimensions. We also discuss a couple of applications of our theoretical results. Finally, numerical studies are carried out to confirm the established theoretical properties.

math.ST↗

arXiv2025

Charge density wave modulated third-order nonlinear Hall effect in 1$T$-VSe$_2$ nanosheets

We report the observation of a pronounced third-order nonlinear Hall effect (NLHE) in 1$T$-phase VSe$_2$ nanosheets, synthesized using chemical vapor deposition (CVD). The nanosheets exhibit a charge density wave (CDW) transition at $\sim$77 K. Detailed angle-resolved and temperature-dependent measurements reveal a strong cubic relationship between the third-harmonic Hall voltage $V_{3ω}^\perp$ and the bias current $I_ω$, persisting up to room temperature. Notably, the third-order NLHE demonstrates a twofold angular dependence and significant enhancement below the CDW transition temperature, indicative of threefold symmetry breaking in the CDW phase. Scaling analysis suggests that the intrinsic contribution from the Berry connection polarizability tensor is substantially increased in the CDW phase, while extrinsic effects dominate at higher temperatures. Our findings highlight the critical role of CDW-induced symmetry breaking in modulating quantum geometric properties and nonlinear transport phenomena in VSe$_2$, paving the way for future explorations in low-dimensional quantum materials.

cond-mat.mes-hall↗

arXiv2025

Giant Third-Order Nonlinearity Induced by the Quantum Metric Quadrupole in Few-Layer WTe2

The quantum geometric properties of topological materials underpin many exotic physical phenomena and applications. Quantum nonlinearity has emerged as a powerful probe for revealing these properties. The Berry curvature dipole in nonmagnetic materials and the quantum metric dipole in antiferromagnets have been explored by studying the second-order nonlinear Hall effect. Although the quadrupole moment of the quantum geometric tensor is theoretically predicted to induce higher-order quantum nonlinearity, the quantum metric quadrupole remains experimentally unexplored. Here, we report the quantum metric quadrupole induced third-order nonlinear longitudinal electrical response in few-layer WTe2, persisting up to room temperature. Angle-resolved third-harmonic current-voltage characteristics are found consistent with the intrinsic crystal symmetry of WTe2. Through temperature variation and scaling analysis, we identify the quantum metric quadrupole as the physical origin of the observed third-order longitudinal nonlinearity. Additionally, we determine the angle dependence of the quantum metric quadrupole, establishing third-order nonlinearity as an efficient method for revealing the quantum metric structure.

cond-mat.mes-hall↗

arXiv2025

Two-way Matrix Autoregressive Model with Thresholds

Recently, matrix-valued time series data have attracted significant attention in the literature with the recognition of threshold nonlinearity representing a significant advance. However, given the fact that a matrix is a two-array structure, it is unfortunate, perhaps even unusual, for the threshold literature to focus on using the same threshold variable for the rows and the columns. In fact, evidence in economic, financial, environmental and other data shows advantages of allowing the possibilities of two different threshold variables (with possibly different threshold parameters for rows and columns), hence the need for a Two-way Matrix AutoRegressive model with Thresholds (2-MART). Naturally, two threshold variables pose new and perhaps even fierce challenges, which might be the reason behind the adoption of only one threshold variable in the literature up to now. In this paper, we develop a comprehensive methodology for the 2-MART model, by overcoming various challenges. Compared with existing models in the literature, the new model can achieve greater dimension reduction, much better model fitting, more accurate predictions, and more plausible interpretations.

stat.ME↗