arXiv subjects

Lin Wang

Publications and source records attributed to Lin Wang.

At least 127 records · Page 7Linked to original sources

arXiv2025

Millisecond Pulsars in M2: New discoveries and a detailed timing analysis

Globular clusters (GCs) offer a unique environment for discovering and studying millisecond pulsars. In this paper, we present a multi-epoch search and detailed timing analysis of millisecond pulsars in the GC M2, using the Five-hundred-meter Aperture Spherical Telescope. We have discovered two new binary millisecond pulsars in M2, designated M2F and M2G, respectively. We provide measurements of the emission properties of all known pulsars in M2, including their polarization profiles, rotation measures, flux densities, scintillation characteristics, and so forth. In particular, we report the first rotation measure at the distance and direction of this cluster. Additionally, we report the first phase-coherent timing solutions for the M2 pulsars. From our Bayesian timing analysis, we have measured their spin and orbital parameters with high precision, including the advance of periastron for M2A and M2E indicating total system masses of 1.75(13) and 1.80(5) solar masses respectively. Using archival data from the Hubble Space Telescope, we have identified an optical counterpart of M2C, which is likely the white dwarf companion of the pulsar. By combining results from optical and radio observations, we have reconstructed the binary evolution track of this system and estimated the cooling age of the companion to be approximately 10\,Myr, making it the youngest white dwarf in any known GC binary pulsars. Furthermore, using the spin period derivatives of M2 pulsars, we have investigated the gravitational potential of the cluster and found that our results strongly support the latest central-stellar-velocity dispersion measurement in M2.

astro-ph.HE↗

arXiv2025

Illuminating Hidden Pulsars: Scintillation-Enhanced Discovery of Two Binary Millisecond Pulsars in M13 with FAST

We conducted a sensitive acceleration search using Fast Fourier Transform (FFT) techniques on full-length and segmented data from 84 observations of the globular cluster M13 with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Employing a low detection threshold (2 $σ$) to maximize sensitivity to faint pulsars, here we report the discovery of two binary millisecond pulsars: J1641+3627G (M13G) and J1641+3627H (M13H). Both pulsars were detected during scintillation-brightened states, revealing systems that would otherwise remain undetected. For M13G, we obtained a phase-connected timing solution spanning 6.4 years, identifying it as a black widow system with an orbital period of 0.12 days hosting an extremely low-mass companion ($\sim 9.9\times 10^{-3}~{ M}_\odot$), though no eclipses were observed. M13H, however, shows significant apparent acceleration but was detected in only 2 of 84 observations; its extremely low detection rate currently prevents constraints on orbital parameters or classification.

astro-ph.HE↗

arXiv2025

PhysPatch: A Physically Realizable and Transferable Adversarial Patch Attack for Multimodal Large Language Models-based Autonomous Driving Systems

Multimodal Large Language Models (MLLMs) are becoming integral to autonomous driving (AD) systems due to their strong vision-language reasoning capabilities. However, MLLMs are vulnerable to adversarial attacks, particularly adversarial patch attacks, which can pose serious threats in real-world scenarios. Existing patch-based attack methods are primarily designed for object detection models and perform poorly when transferred to MLLM-based systems due to the latter's complex architectures and reasoning abilities. To address these limitations, we propose PhysPatch, a physically realizable and transferable adversarial patch framework tailored for MLLM-based AD systems. PhysPatch jointly optimizes patch location, shape, and content to enhance attack effectiveness and real-world applicability. It introduces a semantic-based mask initialization strategy for realistic placement, an SVD-based local alignment loss with patch-guided crop-resize to improve transferability, and a potential field-based mask refinement method. Extensive experiments across open-source, commercial, and reasoning-capable MLLMs demonstrate that PhysPatch significantly outperforms prior methods in steering MLLM-based AD systems toward target-aligned perception and planning outputs. Moreover, PhysPatch consistently places adversarial patches in physically feasible regions of AD scenes, ensuring strong real-world applicability and deployability.

cs.CV↗

arXiv2025

"Harmless to You, Hurtful to Me!": Investigating the Detection of Toxic Languages Grounded in the Perspective of Youth

Risk perception is subjective, and youth's understanding of toxic content differs from that of adults. Although previous research has conducted extensive studies on toxicity detection in social media, the investigation of youth's unique toxicity, i.e., languages perceived as nontoxic by adults but toxic as youth, is ignored. To address this gap, we aim to explore: 1) What are the features of ``youth-toxicity'' languages in social media (RQ1); 2) Can existing toxicity detection techniques accurately detect these languages (RQ2). For these questions, we took Chinese youth as the research target, constructed the first Chinese ``youth-toxicity'' dataset, and then conducted extensive analysis. Our results suggest that youth's perception of these is associated with several contextual factors, like the source of an utterance and text-related features. Incorporating these meta information into current toxicity detection methods significantly improves accuracy overall. Finally, we propose several insights into future research on youth-centered toxicity detection.

cs.CL↗

arXiv2025

DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation

Generating 3D scenes from natural language holds great promise for applications in gaming, film, and design. However, existing methods struggle with automation, 3D consistency, and fine-grained control. We present DreamScene, an end-to-end framework for high-quality and editable 3D scene generation from text or dialogue. DreamScene begins with a scene planning module, where a GPT-4 agent infers object semantics and spatial constraints to construct a hybrid graph. A graph-based placement algorithm then produces a structured, collision-free layout. Based on this layout, Formation Pattern Sampling (FPS) generates object geometry using multi-timestep sampling and reconstructive optimization, enabling fast and realistic synthesis. To ensure global consistent, DreamScene employs a progressive camera sampling strategy tailored to both indoor and outdoor settings. Finally, the system supports fine-grained scene editing, including object movement, appearance changes, and 4D dynamic motion. Experiments demonstrate that DreamScene surpasses prior methods in quality, consistency, and flexibility, offering a practical solution for open-domain 3D content creation. Code and demos are available at https://jahnsonblack.github.io/DreamScene-Full/.

cs.CV↗

arXiv2025

Simplifying Graph Kernels for Efficient

While kernel methods and Graph Neural Networks offer complementary strengths, integrating the two has posed challenges in efficiency and scalability. The Graph Neural Tangent Kernel provides a theoretical bridge by interpreting GNNs through the lens of neural tangent kernels. However, its reliance on deep, stacked layers introduces repeated computations that hinder performance. In this work, we introduce a new perspective by designing the simplified graph kernel, which replaces deep layer stacking with a streamlined $K$-step message aggregation process. This formulation avoids iterative layer-wise propagation altogether, leading to a more concise and computationally efficient framework without sacrificing the expressive power needed for graph tasks. Beyond this simplification, we propose another Simplified Graph Kernel, which draws from Gaussian Process theory to model infinite-width GNNs. Rather than simulating network depth, this kernel analytically computes kernel values based on the statistical behavior of nonlinear activations in the infinite limit. This eliminates the need for explicit architecture simulation, further reducing complexity. Our experiments on standard graph and node classification benchmarks show that our methods achieve competitive accuracy while reducing runtime. This makes them practical alternatives for learning on graphs at scale. Full implementation and reproducibility materials are provided at: https://anonymous.4open.science/r/SGNK-1CE4/.

cs.LG↗

arXiv2025

Optimal Honeypot Ratio and Convergent Fictitious-Play Learning in Signaling Games for CPS Defense

Cyber-Physical Systems (CPSs) are facing a fast-growing wave of attacks. To achieve effective proactive defense, this paper models honeypot deployment as a gamma-fixed signaling game in which node liveness serves as the only signal and normal-node signal gamma is exogenously fixed. We define the gamma-perfect Bayesian-Nash equilibrium (gamma-PBNE). Analytical expressions are obtained for all gamma-PBNEs, revealing three distinct equilibrium regimes that depend on the priori honeypot ratio. Furthermore, the optimal honeypot ratio and signaling strategy that jointly maximize the network average utility are obtained. To capture strategic interaction over time, we develop a discrete-time fictitious-play algorithm that couples Bayesian belief updates with empirical best responses. We prove that, as long as the honeypot ratio is perturbed within a non-degenerate neighbourhood of the optimum, every fictitious-play path converges to the defender-optimal gamma-PBNE. Numerical results confirm the effectiveness of the proposed method and demonstrate its applicability to CPS defense.

eess.SY↗

arXiv2025

On the Dynamics of Invariant Graphs for Dissipative Twist Maps

For two-parameter families of dissipative twist maps, we investigate the dynamics of invariant graphs as well as the thresholds for their existence and breakdown. Our main results are as follows: (1) For arbitrarily small $C^r$ perturbations with $r \geq 1$, invariant graphs with prescribed rotation numbers can be realized by adjusting the parameters; (2) We characterize sharp perturbations that lead to the complete destruction of all invariant graphs; (3) When the perturbation fails to be $C^1$, Lipschitz invariant graphs with non-differentiable points may still persist, even though the Lipschitz norm meets the conditions required by the normally hyperbolic invariant manifold theorem.

math.DS↗

arXiv2025

Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment

360 video captures the complete surrounding scenes with the ultra-large field of view of 360X180. This makes 360 scene understanding tasks, eg, segmentation and tracking, crucial for appications, such as autonomous driving, robotics. With the recent emergence of foundation models, the community is, however, impeded by the lack of large-scale, labelled real-world datasets. This is caused by the inherent spherical properties, eg, severe distortion in polar regions, and content discontinuities, rendering the annotation costly yet complex. This paper introduces Leader360V, the first large-scale, labeled real-world 360 video datasets for instance segmentation and tracking. Our datasets enjoy high scene diversity, ranging from indoor and urban settings to natural and dynamic outdoor scenes. To automate annotation, we design an automatic labeling pipeline, which subtly coordinates pre-trained 2D segmentors and large language models to facilitate the labeling. The pipeline operates in three novel stages. Specifically, in the Initial Annotation Phase, we introduce a Semantic- and Distortion-aware Refinement module, which combines object mask proposals from multiple 2D segmentors with LLM-verified semantic labels. These are then converted into mask prompts to guide SAM2 in generating distortion-aware masks for subsequent frames. In the Auto-Refine Annotation Phase, missing or incomplete regions are corrected either by applying the SDR again or resolving the discontinuities near the horizontal borders. The Manual Revision Phase finally incorporates LLMs and human annotators to further refine and validate the annotations. Extensive user studies and evaluations demonstrate the effectiveness of our labeling pipeline. Meanwhile, experiments confirm that Leader360V significantly enhances model performance for 360 video segmentation and tracking, paving the way for more scalable 360 scene understanding.

cs.CV↗

arXiv2025

Nonlinear bulk photocurrent probe Z2 topological phase transition

Detecting topological phase transitions in bulk is challenging due to the limitations of surface sensitive probes like ARPES. Here, we demonstrate that nonlinear bulk photocurrents, specifically shift and injection currents, serve as effective probes of Z_2 topological transitions. These photocurrents show a robust polarity reversal across the Z_2 phase transition, offering a direct optical signature that distinguishes strong topological phases from weak or trivial ones. This effect originates from a reorganization of key band geometric quantities, the Berry curvature and shift vector, on time-reversal-invariant momentum planes. Using a low energy Dirac model, we trace this behaviour to a band inversion in the time-reversal-invariant momentum plane that drives the topological transition. We validate these findings through tight-binding model for Bi_2Te_3 and first-principles calculations for ZrTe_5 and BiTeI, where the topological phase can be tuned by pressure or temperature. Our results establish nonlinear photocurrent as a sensitive and broadly applicable probe of Z_2 topological phase transitions.

cond-mat.mtrl-sci↗

arXiv2025

BiCo-Fusion: Bidirectional Complementary LiDAR-Camera Fusion for Semantic- and Spatial-Aware 3D Object Detection

3D object detection is an important task that has been widely applied in autonomous driving. To perform this task, a new trend is to fuse multi-modal inputs, i.e., LiDAR and camera. Under such a trend, recent methods fuse these two modalities by unifying them in the same 3D space. However, during direct fusion in a unified space, the drawbacks of both modalities (LiDAR features struggle with detailed semantic information and the camera lacks accurate 3D spatial information) are also preserved, diluting semantic and spatial awareness of the final unified representation. To address the issue, this letter proposes a novel bidirectional complementary LiDAR-camera fusion framework, called BiCo-Fusion that can achieve robust semantic- and spatial-aware 3D object detection. The key insight is to fuse LiDAR and camera features in a bidirectional complementary way to enhance the semantic awareness of the LiDAR and the 3D spatial awareness of the camera. The enhanced features from both modalities are then adaptively fused to build a semantic- and spatial-aware unified representation. Specifically, we introduce Pre-Fusion consisting of a Voxel Enhancement Module (VEM) to enhance the semantic awareness of voxel features from 2D camera features and Image Enhancement Module (IEM) to enhance the 3D spatial awareness of camera features from 3D voxel features. We then introduce Unified Fusion (U-Fusion) to adaptively fuse the enhanced features from the last stage to build a unified representation. Extensive experiments demonstrate the superiority of our BiCo-Fusion against the prior arts. Project page: https://t-ys.github.io/BiCo-Fusion/.

cs.CV↗

arXiv2025

Contest for system observability as an infinitely repeated game

This paper studies a system security problem in the context of observability based on a two-person noncooperative infinitely repeated game. Both the attacker and the defender have means to modify the dimension of the unobservable subspace, which is set as the value function. Utilizing tools from geometric control, we construct the best response sets considering one-step and two-step optimality respectively to maximize or minimize the value function. We establish a unified necessary-and-sufficient condition for Nash equilibrium that holds for both one-step and two-step optimizations. Our analysis further uncovers two evolutionary patterns, lock and loop modes, and shows an asymmetry between defense and attack. The defender can lock the game into equilibrium, whereas the attacker can disrupt it by sacrificing short-term utility for longer-term advantage. Six representative numerical examples corroborate the theoretical results and highlight the complexity of possible game outcomes.

math.OC↗

arXiv2025

The FAST Globular Cluster Pulsar Survey (GC FANS)

By January 2025, 60 pulsars were discovered by the Five-hundred-meter Aperture Spherical radio Telescope globular cluster (GC) pulsar survey (GC FANS), with spin periods spanning 1.98 ms to 3960.72 ms. Of these, 55 are millisecond pulsars (MSPs; $P<30$ ms), while 34 are binaries with orbital periods spanning 0.12 days to 466.47 days. This paper describes GC FANS, a deep, thorough search for pulsars in 41 GCs in the FAST sky ($-14^\circ < δ< 65^\circ$) and describes new discoveries in 14 of them. We present updated timing solutions for M92A, NGC 6712A, M71A, and M71E, all of which are ``spider'' pulsars with short orbital periods. We present new timing solutions for M71B, C, and D. With orbital periods of $\sim$466 and 378 days, M71B and M71C are the widest known GC binaries; these systems resemble the normal wide MSP-He WD systems in the Galactic disk. With a spin period of 101 ms, M71D is in an eccentric ($e\sim$0.63) orbit with an 11-day period and a massive companion; the system has a total mass of $2.63 \pm 0.08 \, M_{\odot}$. These features and its large characteristic age suggest it is a double neutron star system (DNS) formed via massive binary evolution early in the cluster's history, akin to Galactic disk DNSs--unlike other candidate GC DNSs, which typically form dynamically. A comparative analysis of GC pulsar populations within FAST's sky reveals that most clusters (10 of 14) resemble the Galactic disk MSP population, likely due to lower stellar densities.

astro-ph.HE↗

arXiv2025

Phonon-limited valley life times in single-particle bilayer graphene quantum dots

The valley degree of freedom in 2D semiconductors, such as gapped bilayer graphene (BLG) and transition metal dichalcogenides, is a promising carrier of quantum information in the emerging field of valleytronics. While valley dynamics have been extensively studied for moderate band gap 2D~semiconductors using optical spectroscopy techniques, very little is known about valley lifetimes in narrow band gap BLG, which is difficult to study using optical techniques. Here, we report single-particle valley relaxation times ($T_1$) exceeding several microseconds in electrostatically defined BLG quantum dots (QDs) using a pulse-gating technique. The observed dependence of $T_1$ on perpendicular magnetic field can be understood qualitatively and quantitatively by a model in which $T_1$ is limited by electron-phonon coupling. We identify the coupling to acoustic phonons via the bond length change and via the deformation potential as the limiting mechanisms.

cond-mat.mes-hall↗

arXiv2025

Generative Emotion Cause Explanation in Multimodal Conversations

Multimodal conversation, a crucial form of human communication, carries rich emotional content, making the exploration of the causes of emotions within it a research endeavor of significant importance. However, existing research on the causes of emotions typically employs an utterance selection method within a single textual modality to locate causal utterances. This approach remains limited to coarse-grained assessments, lacks nuanced explanations of emotional causation, and demonstrates inadequate capability in identifying multimodal emotional triggers. Therefore, we introduce a task-\textbf{Multimodal Emotion Cause Explanation in Conversation (MECEC)}. This task aims to generate a summary based on the multimodal context of conversations, clearly and intuitively describing the reasons that trigger a given emotion. To adapt to this task, we develop a new dataset (ECEM) based on the MELD dataset. ECEM combines video clips with detailed explanations of character emotions, helping to explore the causal factors behind emotional expression in multimodal conversations. A novel approach, FAME-Net, is further proposed, that harnesses the power of Large Language Models (LLMs) to analyze visual data and accurately interpret the emotions conveyed through facial expressions in videos. By exploiting the contagion effect of facial emotions, FAME-Net effectively captures the emotional causes of individuals engaged in conversations. Our experimental results on the newly constructed dataset show that FAME-Net outperforms several excellent baselines. Code and dataset are available at https://github.com/3222345200/FAME-Net.

cs.CL↗

arXiv2025

Self-supervised Learning of Event-guided Video Frame Interpolation for Rolling Shutter Frames

Most consumer cameras use rolling shutter (RS) exposure, which often leads to distortions such as skew and jelly effects. These videos are further limited by bandwidth and frame rate constraints. In this paper, we explore the potential of event cameras, which offer high temporal resolution. We propose a framework to recover global shutter (GS) high-frame-rate videos without RS distortion by combining an RS camera and an event camera. Due to the lack of real-world datasets, our framework adopts a self-supervised strategy based on a displacement field, a dense 3D spatiotemporal representation of pixel motion during exposure. This enables mutual reconstruction between RS and GS frames and facilitates slow-motion recovery. We combine RS frames with the displacement field to generate GS frames, and integrate inverse mapping and RS frame warping for self-supervision. Experiments on four datasets show that our method removes distortion, reduces bandwidth usage by 94 percent, and achieves 16 ms per frame at 32x interpolation.

cs.CV↗

arXiv2025

Synergy-Informed Design of Platform Trials for Combination Therapies

Combination drug therapies hold significant promise for enhancing treatment efficacy, particularly in fields such as oncology, immunotherapy, and infectious diseases. However, designing clinical trials for these regimens poses unique statistical challenges due to multiple hypothesis testing, shared control groups, and overlapping treatment components that induce complex correlation structures. In this paper, we develop a novel statistical framework tailored for early-phase translational combination therapy trials, with a focus on platform trial designs. Our methodology introduces a generalized Dunnett's procedure that controls false positive rates by accounting for the correlations between treatment arms. Additionally, we propose strategies for power analysis and sample size optimization that leverage preclinical data to estimate effect sizes, synergy parameters, and inter-arm correlations. Simulation studies demonstrate that our approach not only controls various false positive metrics under diverse trial scenarios but also informs optimal allocation ratios to maximize power. A real-data application further illustrates the integration of translational preclinical insights into the clinical trial design process. An open-source R package is provided to support the application of our methods in practice. Overall, our framework offers statistically rigorous guidance for the design of early-phase combination therapy trials, aiming to enhance the efficiency of the bench-to-bedside transition.

stat.ME↗

arXiv2025

Dc-EEMF: Pushing depth-of-field limit of photoacoustic microscopy via decision-level constrained learning

Photoacoustic microscopy holds the potential to measure biomarkers' structural and functional status without labels, which significantly aids in comprehending pathophysiological conditions in biomedical research. However, conventional optical-resolution photoacoustic microscopy (OR-PAM) is hindered by a limited depth-of-field (DoF) due to the narrow depth range focused on a Gaussian beam. Consequently, it fails to resolve sufficient details in the depth direction. Herein, we propose a decision-level constrained end-to-end multi-focus image fusion (Dc-EEMF) to push DoF limit of PAM. The DC-EEMF method is a lightweight siamese network that incorporates an artifact-resistant channel-wise spatial frequency as its feature fusion rule. The meticulously crafted U-Net-based perceptual loss function for decision-level focus properties in end-to-end fusion seamlessly integrates the complementary advantages of spatial domain and transform domain methods within Dc-EEMF. This approach can be trained end-to-end without necessitating post-processing procedures. Experimental results and numerical analyses collectively demonstrate our method's robust performance, achieving an impressive fusion result for PAM images without a substantial sacrifice in lateral resolution. The utilization of Dc-EEMF-powered PAM has the potential to serve as a practical tool in preclinical and clinical studies requiring extended DoF for various applications.

eess.IV↗