arXiv subjects

Wenbin Li

Publications and source records attributed to Wenbin Li.

At least 73 records · Page 4Linked to original sources

arXiv2025

Quantum spin excitations in a dual-core magnetic molecule

Magnetic excitations are important quantum phenomena in magnetic systems and have been widely studied in individual magnetic atoms and molecules as well as their assembled structures over the past few decades. Using scanning tunneling microscopy/spectroscopy (STM/S) combined with density functional theory (DFT) and the state-of-the-art ab initio wavefunction calculations, we investigated the properties of a novel dual-core Cr2Br6 molecule, which consists of two Cr ions coupled via superexchange through a single near-90° Cr-Br-Cr scissors bond. Under zero magnetic field, we observed a Fano peak with multi-steps through STS. When an external magnetic field is applied, some steps exhibit additional splitting, while others change little. We find that the Cr2Br6, exhibits a spin-degenerate ground state, and the complex peak splitting arises from the coexistence of vibrational and magnetic excitations in the molecule. Our results reveal rich quantum spin behavior in a well-defined two-core magnetic trihalide complex at the atomic scale, offering not only a minimal model for superexchange-coupled multi-spin quantum excitations but also a possible foundational unit for future molecule-based quantum functionalities.

cond-mat.mtrl-sci↗

arXiv2025

AT-Drone: Benchmarking Adaptive Teaming in Multi-Drone Pursuit

Adaptive teaming-the capability of agents to effectively collaborate with unfamiliar teammates without prior coordination-is widely explored in virtual video games but overlooked in real-world multi-robot contexts. Yet, such adaptive collaboration is crucial for real-world applications, including border surveillance, search-and-rescue, and counter-terrorism operations. To address this gap, we introduce AT-Drone, the first dedicated benchmark explicitly designed to facilitate comprehensive training and evaluation of adaptive teaming strategies in multi-drone pursuit scenarios. AT-Drone makes the following key contributions: (1) An adaptable simulation environment configurator that enables intuitive and rapid setup of adaptive teaming multi-drone pursuit tasks, including four predefined pursuit environments. (2) A streamlined real-world deployment pipeline that seamlessly translates simulation insights into practical drone evaluations using edge devices and Crazyflie drones. (3) A novel algorithm zoo integrated with a distributed training framework, featuring diverse algorithms explicitly tailored, for the first time, to multi-pursuer and multi-evader settings. (4) Standardized evaluation protocols with newly designed unseen drone zoos, explicitly designed to rigorously assess the performance of adaptive teaming. Comprehensive experimental evaluations across four progressively challenging multi-drone pursuit scenarios confirm AT-Drone's effectiveness in advancing adaptive teaming research. Real-world drone experiments further validate its practical feasibility and utility for realistic robotic operations. Videos, code and weights are available at \url{https://sites.google.com/view/at-drone}.

cs.RO↗

arXiv2025

Efficient Last-iterate Convergence Algorithms in Solving Games

To establish last-iterate convergence for Counterfactual Regret Minimization (CFR) algorithms in learning a Nash equilibrium (NE) of extensive-form games (EFGs), recent studies reformulate learning an NE of the original EFG as learning the NEs of a sequence of (perturbed) regularized EFGs. Consequently, proving last-iterate convergence in solving the original EFG reduces to proving last-iterate convergence in solving (perturbed) regularized EFGs. However, the empirical convergence rates of the algorithms in these studies are suboptimal, since they do not utilize Regret Matching (RM)-based CFR algorithms to solve perturbed EFGs, which are known the exceptionally fast empirical convergence rates. Additionally, since solving multiple perturbed regularized EFGs is required, fine-tuning across all such games is infeasible, making parameter-free algorithms highly desirable. In this paper, we prove that CFR$^+$, a classical parameter-free RM-based CFR algorithm, achieves last-iterate convergence in learning an NE of perturbed regularized EFGs. Leveraging CFR$^+$ to solve perturbed regularized EFGs, we get Reward Transformation CFR$^+$ (RTCFR$^+$). Importantly, we extend prior work on the parameter-free property of CFR$^+$, enhancing its stability, which is crucial for the empirical convergence of RTCFR$^+$. Experiments show that RTCFR$^+$ significantly outperforms existing algorithms with theoretical last-iterate convergence guarantees.

cs.GT↗

arXiv2025

Robust Dataset Distillation by Matching Adversarial Trajectories

Dataset distillation synthesizes compact datasets that enable models to achieve performance comparable to training on the original large-scale datasets. However, existing distillation methods overlook the robustness of the model, resulting in models that are vulnerable to adversarial attacks when trained on distilled data. To address this limitation, we introduce the task of ``robust dataset distillation", a novel paradigm that embeds adversarial robustness into the synthetic datasets during the distillation process. We propose Matching Adversarial Trajectories (MAT), a method that integrates adversarial training into trajectory-based dataset distillation. MAT incorporates adversarial samples during trajectory generation to obtain robust training trajectories, which are then used to guide the distillation process. As experimentally demonstrated, even through natural training on our distilled dataset, models can achieve enhanced adversarial robustness while maintaining competitive accuracy compared to existing distillation methods. Our work highlights robust dataset distillation as a new and important research direction and provides a strong baseline for future research to bridge the gap between efficient training and adversarial robustness.

cs.CV↗

arXiv2025

WeakMedSAM: Weakly-Supervised Medical Image Segmentation via SAM with Sub-Class Exploration and Prompt Affinity Mining

We have witnessed remarkable progress in foundation models in vision tasks. Currently, several recent works have utilized the segmenting anything model (SAM) to boost the segmentation performance in medical images, where most of them focus on training an adaptor for fine-tuning a large amount of pixel-wise annotated medical images following a fully supervised manner. In this paper, to reduce the labeling cost, we investigate a novel weakly-supervised SAM-based segmentation model, namely WeakMedSAM. Specifically, our proposed WeakMedSAM contains two modules: 1) to mitigate severe co-occurrence in medical images, a sub-class exploration module is introduced to learn accurate feature representations. 2) to improve the quality of the class activation maps, our prompt affinity mining module utilizes the prompt capability of SAM to obtain an affinity map for random-walk refinement. Our method can be applied to any SAM-like backbone, and we conduct experiments with SAMUS and EfficientSAM. The experimental results on three popularly-used benchmark datasets, i.e., BraTS 2019, AbdomenCT-1K, and MSD Cardiac dataset, show the promising results of our proposed WeakMedSAM. Our code is available at https://github.com/wanghr64/WeakMedSAM.

cs.CV↗

arXiv2025

Quantum Geometric Origin of Strain-Induced Ferroelectric Phase Transitions

Strain-regulated ferroelectric (FE) materials have long attracted significant attention due to their diverse applications. While soft-phonon theory and the (pseudo) Jahn-Teller effect have achieved considerable success in providing phenomenological descriptions and general understanding, the detailed connection between these perspectives and their microscopic dependence on strain regulation remains unclear. Here, under the framework of density-functional perturbation theory (DFPT), we demonstrate that the Berry curvature of electron-phonon coupling (EPC) plays a pivotal role in the interatomic force matrix (IFM). A subsequent model analysis shows that external strain can reverse the polarity of the EPC Berry curvature in (quasi)-degenerate electronic subsystems through band inversion, thereby directly leading to phonon softening. The general theory is then applied to the BiOCl monolayer as a benchmark, which offers an accurate description of the density functional theory (DFT) calculations. This mechanism is further observed across a broad range of materials through ab initio calculations, providing an insightful perspective on EPC quantum geometry in lattice dynamics and FE phase transitions.

cond-mat.mtrl-sci↗

arXiv2025

Quantum Geometric Origin of Strain-Tunable Giant Second-Harmonic Generation in Bi$_2$O$_2$X (X=S, Se, Te)

Two-dimensional (2D) materials with giant nonlinear optical (NLO) responses are essential for the development of advanced on-chip NLO devices. Using first-principles calculations, we predict a remarkable strain-induced enhancement of second-harmonic generation (SHG) in the high-performance 2D semiconductors Bi$_2$O$_2$X (X = S, Se, Te). The SHG susceptibilities of Bi$_2$O$_2$X under strain are on the order of 1~nm/V, rivalling the highest values reported among 2D materials. This giant SHG response originates from gauge-invariant geometric quantities, including the quantum metric, shift vector, and triple phase product. The strain also induces a bandgap variation in Bi$_2$O$_2$X. Intriguingly, in Bi$_2$O$_2$Te, strain-induced bandgap tuning drives a transition from a semiconductor to a half-metal, and ultimately to a polar metal. Our findings present a unique platform that combines strain-tunable bandgap engineering with exceptional NLO properties, while also highlighting the crucial role of quantum geometry in enhancing SHG.

cond-mat.mtrl-sci↗

arXiv2024

CausalTAD: Causal Implicit Generative Model for Debiased Online Trajectory Anomaly Detection

Trajectory anomaly detection, aiming to estimate the anomaly risk of trajectories given the Source-Destination (SD) pairs, has become a critical problem for many real-world applications. Existing solutions directly train a generative model for observed trajectories and calculate the conditional generative probability $P({T}|{C})$ as the anomaly risk, where ${T}$ and ${C}$ represent the trajectory and SD pair respectively. However, we argue that the observed trajectories are confounded by road network preference which is a common cause of both SD distribution and trajectories. Existing methods ignore this issue limiting their generalization ability on out-of-distribution trajectories. In this paper, we define the debiased trajectory anomaly detection problem and propose a causal implicit generative model, namely CausalTAD, to solve it. CausalTAD adopts do-calculus to eliminate the confounding bias of road network preference and estimates $P({T}|do({C}))$ as the anomaly criterion. Extensive experiments show that CausalTAD can not only achieve superior performance on trained trajectories but also generally improve the performance of out-of-distribution data, with improvements of $2.1\% \sim 5.7\%$ and $10.6\% \sim 32.7\%$ respectively.

cs.LG↗

arXiv2024

Effective and Efficient Representation Learning for Flight Trajectories

Flight trajectory data plays a vital role in the traffic management community, especially for downstream tasks such as trajectory prediction, flight recognition, and anomaly detection. Existing works often utilize handcrafted features and design models for different tasks individually, which heavily rely on domain expertise and are hard to extend. We argue that different flight analysis tasks share the same useful features of the trajectory. Jointly learning a unified representation for flight trajectories could be beneficial for improving the performance of various tasks. However, flight trajectory representation learning (TRL) faces two primary challenges, \ie unbalanced behavior density and 3D spatial continuity, which disable recent general TRL methods. In this paper, we propose Flight2Vec , a flight-specific representation learning method to address these challenges. Specifically, a behavior-adaptive patching mechanism is used to inspire the learned representation to pay more attention to behavior-dense segments. Moreover, we introduce a motion trend learning technique that guides the model to memorize not only the precise locations, but also the motion trend to generate better representations. Extensive experimental results demonstrate that Flight2Vec significantly improves performance in downstream tasks such as flight trajectory prediction, flight recognition, and anomaly detection.

cs.AI↗

arXiv2024

3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities

3D Gaussian Splatting (3DGS) has emerged as a prominent technique with the potential to become a mainstream method for 3D representations. It can effectively transform multi-view images into explicit 3D Gaussian through efficient training, and achieve real-time rendering of novel views. This survey aims to analyze existing 3DGS-related works from multiple intersecting perspectives, including related tasks, technologies, challenges, and opportunities. The primary objective is to provide newcomers with a rapid understanding of the field and to assist researchers in methodically organizing existing technologies and challenges. Specifically, we delve into the optimization, application, and extension of 3DGS, categorizing them based on their focuses or motivations. Additionally, we summarize and classify nine types of technical modules and corresponding improvements identified in existing works. Based on these analyses, we further examine the common challenges and technologies across various tasks, proposing potential research opportunities.

cs.CV↗

arXiv2024

Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP

Contrastive Language-Image Pretraining (CLIP) has been widely used in vision tasks. Notably, CLIP has demonstrated promising performance in few-shot learning (FSL). However, existing CLIP-based methods in training-free FSL (i.e., without the requirement of additional training) mainly learn different modalities independently, leading to two essential issues: 1) severe anomalous match in image modality; 2) varying quality of generated text prompts. To address these issues, we build a mutual guidance mechanism, that introduces an Image-Guided-Text (IGT) component to rectify varying quality of text prompts through image representations, and a Text-Guided-Image (TGI) component to mitigate the anomalous match of image modality through text representations. By integrating IGT and TGI, we adopt a perspective of Text-Image Mutual guidance Optimization, proposing TIMO. Extensive experiments show that TIMO significantly outperforms the state-of-the-art (SOTA) training-free method. Additionally, by exploring the extent of mutual guidance, we propose an enhanced variant, TIMO-S, which even surpasses the best training-required methods by 0.33% with approximately 100 times less time cost. Our code is available at https://github.com/lyymuwu/TIMO.

cs.CV↗

arXiv2024

Highly Polarizable Semiconductors and Universal Origin of Ferroelectricity in Materials with a Litharge-Type Structural Unit

We discover that a large family of [Pb$_2$F$_2$]- and [Bi$_2$O$_2$]-based mixed-anion materials with a litharge-type structural unit are highly polarizable layered semiconductors on the edge of ferroelectricity. First-principles calculations demonstrate that in this family of materials, compounds as diverse as PbFBr, BiOCl, BiCuOSe, Bi$_2$OS$_2$, and Bi$_5$O$_4$S$_3$Cl exhibit static dielectric constants an order of magnitude higher than typical semiconductors. Additionally, they undergo a ferroelectric transition when subjected to a few percent of tensile strain. The ferroelectric transitions of these materials are found to have a universal origin in the strong cross-bandgap hybridization of the cation $p$ orbitals, enabled by the cation 6s$^2$ lone-pair electrons and the litharge-type structure of the [Pb$_2$F$_2$] and [Bi$_2$O$_2$] layers, as demonstrated by the strain-induced ferroelectric transition in the archetypal litharge $α$-PbO. These results establish materials with a litharge-type structural unit as a large and versatile family of highly polarizable layered semiconductors in proximity to ferroelectricity, offering vast opportunities for multifunctional materials design.

cond-mat.mtrl-sci↗

arXiv2024

Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training

Recent advancements in vision-language pre-training via contrastive learning have significantly improved performance across computer vision tasks. However, in the medical domain, obtaining multimodal data is often costly and challenging due to privacy, sensitivity, and annotation complexity. To mitigate data scarcity while boosting model performance, we introduce \textbf{Uni-Mlip}, a unified self-supervision framework specifically designed to enhance medical vision-language pre-training. Uni-Mlip seamlessly integrates cross-modality, uni-modality, and fused-modality self-supervision techniques at the data-level and the feature-level. Additionally, Uni-Mlip tailors uni-modal image self-supervision to accommodate the unique characteristics of medical images. Our experiments across datasets of varying scales demonstrate that Uni-Mlip significantly surpasses current state-of-the-art methods in three key downstream tasks: image-text retrieval, image classification, and visual question answering (VQA).

cs.CV↗

arXiv2024

A data-dependent regularization method based on the graph Laplacian

We investigate a variational method for ill-posed problems, named $\texttt{graphLa+}Ψ$, which embeds a graph Laplacian operator in the regularization term. The novelty of this method lies in constructing the graph Laplacian based on a preliminary approximation of the solution, which is obtained using any existing reconstruction method $Ψ$ from the literature. As a result, the regularization term is both dependent on and adaptive to the observed data and noise. We demonstrate that $\texttt{graphLa+}Ψ$ is a regularization method and rigorously establish both its convergence and stability properties. We present selected numerical experiments in 2D computerized tomography, wherein we integrate the $\texttt{graphLa+}Ψ$ method with various reconstruction techniques $Ψ$, including Filter Back Projection ($\texttt{graphLa+FBP}$), standard Tikhonov ($\texttt{graphLa+Tik}$), Total Variation ($\texttt{graphLa+TV}$), and a trained deep neural network ($\texttt{graphLa+Net}$). The $\texttt{graphLa+}Ψ$ approach significantly enhances the quality of the approximated solutions for each method $Ψ$. Notably, $\texttt{graphLa+Net}$ is outperforming, offering a robust and stable application of deep neural networks in solving inverse problems.

math.NA↗

arXiv2024

Neural Implicit Representation for Highly Dynamic LiDAR Mapping and Odometry

Recent advancements in Simultaneous Localization and Mapping (SLAM) have increasingly highlighted the robustness of LiDAR-based techniques. At the same time, Neural Radiance Fields (NeRF) have introduced new possibilities for 3D scene reconstruction, exemplified by SLAM systems. Among these, NeRF-LOAM has shown notable performance in NeRF-based SLAM applications. However, despite its strengths, these systems often encounter difficulties in dynamic outdoor environments due to their inherent static assumptions. To address these limitations, this paper proposes a novel method designed to improve reconstruction in highly dynamic outdoor scenes. Based on NeRF-LOAM, the proposed approach consists of two primary components. First, we separate the scene into static background and dynamic foreground. By identifying and excluding dynamic elements from the mapping process, this segmentation enables the creation of a dense 3D map that accurately represents the static background only. The second component extends the octree structure to support multi-resolution representation. This extension not only enhances reconstruction quality but also aids in the removal of dynamic objects identified by the first module. Additionally, Fourier feature encoding is applied to the sampled points, capturing high-frequency information and leading to more complete reconstruction results. Evaluations on various datasets demonstrate that our method achieves more competitive results compared to current state-of-the-art approaches.

cs.CV↗

arXiv2024

AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models

Detecting anomaly edges for dynamic graphs aims to identify edges significantly deviating from the normal pattern and can be applied in various domains, such as cybersecurity, financial transactions and AIOps. With the evolving of time, the types of anomaly edges are emerging and the labeled anomaly samples are few for each type. Current methods are either designed to detect randomly inserted edges or require sufficient labeled data for model training, which harms their applicability for real-world applications. In this paper, we study this problem by cooperating with the rich knowledge encoded in large language models(LLMs) and propose a method, namely AnomalyLLM. To align the dynamic graph with LLMs, AnomalyLLM pre-trains a dynamic-aware encoder to generate the representations of edges and reprograms the edges using the prototypes of word embeddings. Along with the encoder, we design an in-context learning framework that integrates the information of a few labeled samples to achieve few-shot anomaly detection. Experiments on four datasets reveal that AnomalyLLM can not only significantly improve the performance of few-shot anomaly detection, but also achieve superior results on new anomalies without any update of model parameters.

cs.LG↗

arXiv2024

Phase engineering of giant second harmonic generation in Bi$_2$O$_2$Se

Two-dimensional (2D) materials with remarkable second-harmonic generation (SHG) hold promise for future on-chip nonlinear optics. Relevant materials with both giant SHG response and environmental stability are long-sought targets. Here, we demonstrate the enormous SHG from the phase engineering of a high-performance semiconductor, Bi$_2$O$_2$Se (BOS), under uniaxial strain. SHG signals captured in strained 20 nm-BOS films exceed those of NbOI$_2$ and NbOCl$_2$ of similar thickness by a factor of 10, and are four orders of magnitude higher than monolayer-MoS$_2$, resulting in a significant second-order nonlinear susceptibility on the order of 1 nm V$^{-1}$. Intriguingly, the strain enables continuous adjustment of the ferroelectric phase transition across room temperature. Consequently, an exceptionally large tunability of SHG, approximately six orders of magnitude, is achieved through strain or thermal modulation. This colossal SHG, originating from the geometric phase of Bloch wave functions and coupled with sensitive tunability through multiple approaches in this air-stable 2D semiconductor, opens new possibilities for designing chip-scale, switchable nonlinear optical devices.

cond-mat.mtrl-sci↗

arXiv2024

PORCA: Root Cause Analysis with Partially Observed Data

Root Cause Analysis (RCA) aims at identifying the underlying causes of system faults by uncovering and analyzing the causal structure from complex systems. It has been widely used in many application domains. Reliable diagnostic conclusions are of great importance in mitigating system failures and financial losses. However, previous studies implicitly assume a full observation of the system, which neglect the effect of partial observation (i.e., missing nodes and latent malfunction). As a result, they fail in deriving reliable RCA results. In this paper, we unveil the issues of unobserved confounders and heterogeneity in partial observation and come up with a new problem of root cause analysis with partially observed data. To achieve this, we propose PORCA, a novel RCA framework which can explore reliable root causes under both unobserved confounders and unobserved heterogeneity. PORCA leverages magnified score-based causal discovery to efficiently optimize acyclic directed mixed graph under unobserved confounders. In addition, we also develop a heterogeneity-aware scheduling strategy to provide adaptive sample weights. Extensive experimental results on one synthetic and two real-world datasets demonstrate the effectiveness and superiority of the proposed framework.

cs.AI↗