arXiv subjects

Bo Liu

Publications and source records attributed to Bo Liu.

At least 235 records · Page 13Linked to original sources

arXiv2025

StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging

In this paper, we introduce StreakNet-Arch, a real-time, end-to-end binary-classification framework based on our self-developed Underwater Carrier LiDAR-Radar (UCLR) that embeds Self-Attention and our novel Double Branch Cross Attention (DBC-Attention) to enhance scatter suppression. Under controlled water tank validation conditions, StreakNet-Arch with Self-Attention or DBC-Attention outperforms traditional bandpass filtering and achieves higher $F_1$ scores than learning-based MP networks and CNNs at comparable model size and complexity. Real-time benchmarks on an NVIDIA RTX 3060 show a constant Average Imaging Time (54 to 84 ms) regardless of frame count, versus a linear increase (58 to 1,257 ms) for conventional methods. To facilitate further research, we contribute a publicly available streak-tube camera image dataset contains 2,695,168 real-world underwater 3D point cloud data. More importantly, we validate our UCLR system in a South China Sea trial, reaching an error of 46mm for 3D target at 1,000 m depth and 20 m range. Source code and data are available at https://github.com/BestAnHongjun/StreakNet .

cs.CV↗

arXiv2025

Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy

Depth estimation is a cornerstone of 3D reconstruction and plays a vital role in minimally invasive endoscopic surgeries. However, most current depth estimation networks rely on traditional convolutional neural networks, which are limited in their ability to capture global information. Foundation models offer a promising approach to enhance depth estimation, but those models currently available are primarily trained on natural images, leading to suboptimal performance when applied to endoscopic images. In this work, we introduce a novel fine-tuning strategy for the Depth Anything Model and integrate it with an intrinsic-based unsupervised monocular depth estimation framework. Our approach includes a low-rank adaptation technique based on random vectors, which improves the model's adaptability to different scales. Additionally, we propose a residual block built on depthwise separable convolution to compensate for the transformer's limited ability to capture local features. Our experimental results on the SCARED dataset and Hamlyn dataset show that our method achieves state-of-the-art performance while minimizing the number of trainable parameters. Applying this method in minimally invasive endoscopic surgery can enhance surgeons' spatial awareness, thereby improving the precision and safety of the procedures.

cs.CV↗

arXiv2025

Spin excitations arising from anisotropic Dirac spinons in YCu$_3$(OD)$_6$Br$_2$[Br$_{0.33}$(OD)$_{0.67}$]

A Dirac quantum spin liquid hosts Dirac spinons, which are low-energy fractionalized neutral quasiparticles with spin 1/2 that obey the Dirac equation. Recent inelastic neutron scattering studies have revealed a cone spin continuum in YCu$_3$(OD)$_6$Br$_2$[Br$_{x}$(OD)$_{1-x}$], consistent with the convolution of two Dirac spinons. In this work, we further studied spin excitations using the inelastic neutron scattering technique. The width of low-energy spin excitations shows a linear temperature dependence, which can be explained by spinon-spinon interactions with a Dirac dispersion. Polarized neutron scattering measurements reveal that in-plane magnetic fluctuations are about 1.5 times stronger than the out-of-plane ones, suggesting the presence of Dzyaloshinskii-Moriya interaction and consistent with our theoretical modeling and simulations. Moreover, the high-energy spin excitations around 14 meV agree with the one-pair spinon-antispinon excitations in Raman studies. The real part of the dynamical susceptibility derived from the Kramers-Kronig relationship also agrees with the Knight shift measured by nuclear magnetic resonance, clearly demonstrating the negligible effects of magnetic impurities on static susceptibility. These results provide a rare example in studying quantum-spin-liquid materials where different experimental techniques can be directly compared, and they give further insights for the possible Dirac quantum spin liquid in this system.

cond-mat.str-el↗

arXiv2025

HiLa: Hierarchical Vision-Language Collaboration for Cancer Survival Prediction

Survival prediction using whole-slide images (WSIs) is crucial in cancer re-search. Despite notable success, existing approaches are limited by their reliance on sparse slide-level labels, which hinders the learning of discriminative repre-sentations from gigapixel WSIs. Recently, vision language (VL) models, which incorporate additional language supervision, have emerged as a promising solu-tion. However, VL-based survival prediction remains largely unexplored due to two key challenges. First, current methods often rely on only one simple lan-guage prompt and basic cosine similarity, which fails to learn fine-grained associ-ations between multi-faceted linguistic information and visual features within WSI, resulting in inadequate vision-language alignment. Second, these methods primarily exploit patch-level information, overlooking the intrinsic hierarchy of WSIs and their interactions, causing ineffective modeling of hierarchical interac-tions. To tackle these problems, we propose a novel Hierarchical vision-Language collaboration (HiLa) framework for improved survival prediction. Specifically, HiLa employs pretrained feature extractors to generate hierarchical visual features from WSIs at both patch and region levels. At each level, a series of language prompts describing various survival-related attributes are constructed and aligned with visual features via Optimal Prompt Learning (OPL). This ap-proach enables the comprehensive learning of discriminative visual features cor-responding to different survival-related attributes from prompts, thereby improv-ing vision-language alignment. Furthermore, we introduce two modules, i.e., Cross-Level Propagation (CLP) and Mutual Contrastive Learning (MCL) to maximize hierarchical cooperation by promoting interactions and consistency be-tween patch and region levels. Experiments on three TCGA datasets demonstrate our SOTA performance.

cs.CV↗

arXiv2025

Lion Secretly Solves Constrained Optimization: As Lyapunov Predicts

Lion (Evolved Sign Momentum), a new optimizer discovered through program search, has shown promising results in training large AI models. It performs comparably or favorably to AdamW but with greater memory efficiency. As we can expect from the results of a random search program, Lion incorporates elements from several existing algorithms, including signed momentum, decoupled weight decay, Polak, and Nesterov momentum, but does not fit into any existing category of theoretically grounded optimizers. Thus, even though Lion appears to perform well as a general-purpose optimizer for a wide range of tasks, its theoretical basis remains uncertain. This lack of theoretical clarity limits opportunities to further enhance and expand Lion's efficacy. This work aims to demystify Lion. Based on both continuous-time and discrete-time analysis, we demonstrate that Lion is a theoretically novel and principled approach for minimizing a general loss function $f(x)$ while enforcing a bound constraint $\|x\|_\infty \leq 1/λ$. Lion achieves this through the incorporation of decoupled weight decay, where $λ$ represents the weight decay coefficient. Our analysis is made possible by the development of a new Lyapunov function for the Lion updates. It applies to a broader family of Lion-$κ$ algorithms, where the $\text{sign}(\cdot)$ operator in Lion is replaced by the subgradient of a convex function $κ$, leading to the solution of a general composite optimization problem of $\min_x f(x) + κ^*(x)$. Our findings provide valuable insights into the dynamics of Lion and pave the way for further improvements and extensions of Lion-related algorithms.

cs.LG↗

arXiv2025

MamNet: A Novel Hybrid Model for Time-Series Forecasting and Frequency Pattern Analysis in Network Traffic

The abnormal fluctuations in network traffic may indicate potential security threats or system failures. Therefore, efficient network traffic prediction and anomaly detection methods are crucial for network security and traffic management. This paper proposes a novel network traffic prediction and anomaly detection model, MamNet, which integrates time-domain modeling and frequency-domain feature extraction. The model first captures the long-term dependencies of network traffic through the Mamba module (time-domain modeling), and then identifies periodic fluctuations in the traffic using Fourier Transform (frequency-domain feature extraction). In the feature fusion layer, multi-scale information is integrated to enhance the model's ability to detect network traffic anomalies. Experiments conducted on the UNSW-NB15 and CAIDA datasets demonstrate that MamNet outperforms several recent mainstream models in terms of accuracy, recall, and F1-Score. Specifically, it achieves an improvement of approximately 2% to 4% in detection performance for complex traffic patterns and long-term trend detection. The results indicate that MamNet effectively captures anomalies in network traffic across different time scales and is suitable for anomaly detection tasks in network security and traffic management. Future work could further optimize the model structure by incorporating external network event information, thereby improving the model's adaptability and stability in complex network environments.

cs.LG↗

arXiv2025

Direct imaging of quantum interference and Non-Abelian entanglement in Hopfion: an magnetic soliton possess loop-like anyonic properties

This work provides the first experimental elucidation of quantum topological effects in individual hopfions, establishing their potential as building blocks for three-dimensional topological quantum spintronics. The observed Non-Abelian characteristics suggest pathways toward fault-tolerant quantum operations through controlled hopfion braiding in engineered magnetic metamaterials.

cond-mat.mes-hall↗

arXiv2025

ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid Motions

Editing images with instructions to reflect non-rigid motions, camera viewpoint shifts, object deformations, human articulations, and complex interactions, poses a challenging yet underexplored problem in computer vision. Existing approaches and datasets predominantly focus on static scenes or rigid transformations, limiting their capacity to handle expressive edits involving dynamic motion. To address this gap, we introduce ByteMorph, a comprehensive framework for instruction-based image editing with an emphasis on non-rigid motions. ByteMorph comprises a large-scale dataset, ByteMorph-6M, and a strong baseline model built upon the Diffusion Transformer (DiT), named ByteMorpher. ByteMorph-6M includes over 6 million high-resolution image editing pairs for training, along with a carefully curated evaluation benchmark ByteMorph-Bench. Both capture a wide variety of non-rigid motion types across diverse environments, human figures, and object categories. The dataset is constructed using motion-guided data generation, layered compositing techniques, and automated captioning to ensure diversity, realism, and semantic coherence. We further conduct a comprehensive evaluation of recent instruction-based image editing methods from both academic and commercial domains.

cs.CV↗

arXiv2025

Clock Synchronization for Drone-Based Entanglement Quantum Key Distribution

Drone-based entanglement distribution provides full spatiotemporal coverage for quantum networks, enabling quantum key distribution (QKD) in dynamic environments. The security of QKD fundamentally depends on high-fidelity quantum state measurements, for which high-precision clock synchronization is indispensable, as timing jitter is inversely correlated with quantum state fidelity. However, drone-based clock synchronization is constrained by SWaP (Size, Weight, and Power) limitations and dynamic mobility effects. Here, we propose a synchronization protocol for drone-based entanglement distribution, leveraging nanosecond-accurate Global Navigation Satellite System (GNSS) timing and entanglement-based timing correction to overcome SWaP constraints. Experimental results demonstrate 24 ps RMS synchronization in simulated free-space quantum channels with distance dynamics, without requiring precision reference clock. Our protocol enables drone-based entanglement distribution, paving the way for seamless wide-area and local-area quantum internet.

quant-ph↗

arXiv2025

Natural Language Reinforcement Learning

Artificial intelligence progresses towards the "Era of Experience," where agents are expected to learn from continuous, grounded interaction. We argue that traditional Reinforcement Learning (RL), which typically represents value as a scalar, can restrict agent's deep understanding of environments and hinders the active, deliberative learning crucial for navigating this new paradigm. To address the issue, we introduce Natural Language Reinforcement Learning (NLRL), a framework that extends RL principles into natural language counterparts. Central to NLRL is the Language Value Function (LVF), which redefines value as an interpretable linguistic narrative articulating the rationale behind an evaluation. NLRL further extends this concept to core RL components, including policy, the Bellman equation, and policy iteration. Leveraging recent advancements in Large Language Models (LLMs), NLRL can be practically implemented to achieve RL-like policy and value training through unsupervised environment interactions. Experiments over 4 multi-step agentic tasks demonstrate NLRL's effectiveness, efficiency, and its potential to foster deeper understanding and more active learning strategies.

cs.LG↗

arXiv2025

THE WASTIVE: An Interactive Ebb and Flow of Digital Fabrication Waste

What if digital fabrication waste could observe the world? What would they see? What would they say? "THE WASTIVE" reimagines digital fabrication waste as sentient observers, giving them a poetic voice through interactive art. As viewers approach, the installation awakens, mimicking the rhythmic ebb and flow of ocean waves - a silent dialogue where discarded materials "observe" and respond to human presence. These interactions echo the gentle murmurs of the sea, transforming technological residue into a reflective, sensory experience. Through this artistic contemplation, "THE WASTIVE" invites audiences to reconsider their creative processes and consumption habits. It serves as a poetic call for more mindful, sustainable practices, provoking deeper reflections on our interconnectedness with the environment.

cs.MM↗

arXiv2025

TextArena

TextArena is an open-source collection of competitive text-based games for training and evaluation of agentic behavior in Large Language Models (LLMs). It spans 57+ unique environments (including single-player, two-player, and multi-player setups) and allows for easy evaluation of model capabilities via an online-play system (against humans and other submitted models) with real-time TrueSkill scores. Traditional benchmarks rarely assess dynamic social skills such as negotiation, theory of mind, and deception, creating a gap that TextArena addresses. Designed with research, community and extensibility in mind, TextArena emphasizes ease of adding new games, adapting the framework, testing models, playing against the models, and training models. Detailed documentation of environments, games, leaderboard, and examples are available on https://github.com/LeonGuertler/TextArena and https://www.textarena.ai/.

cs.CL↗

arXiv2025

TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection

Text anomaly detection is crucial for identifying spam, misinformation, and offensive language in natural language processing tasks. Despite the growing adoption of embedding-based methods, their effectiveness and generalizability across diverse application scenarios remain under-explored. To address this, we present TAD-Bench, a comprehensive benchmark designed to systematically evaluate embedding-based approaches for text anomaly detection. TAD-Bench integrates multiple datasets spanning different domains, combining state-of-the-art embeddings from large language models with a variety of anomaly detection algorithms. Through extensive experiments, we analyze the interplay between embeddings and detection methods, uncovering their strengths, weaknesses, and applicability to different tasks. These findings offer new perspectives on building more robust, efficient, and generalizable anomaly detection systems for real-world applications.

cs.CL↗

arXiv2025

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models

Understanding surveillance video content remains a critical yet underexplored challenge in vision-language research, particularly due to its real-world complexity, irregular event dynamics, and safety-critical implications. In this work, we introduce SurveillanceVQA-589K, the largest open-ended video question answering benchmark tailored to the surveillance domain. The dataset comprises 589,380 QA pairs spanning 12 cognitively diverse question types, including temporal reasoning, causal inference, spatial understanding, and anomaly interpretation, across both normal and abnormal video scenarios. To construct the benchmark at scale, we design a hybrid annotation pipeline that combines temporally aligned human-written captions with Large Vision-Language Model-assisted QA generation using prompt-based techniques. We also propose a multi-dimensional evaluation protocol to assess contextual, temporal, and causal comprehension. We evaluate eight LVLMs under this framework, revealing significant performance gaps, especially in causal and anomaly-related tasks, underscoring the limitations of current models in real-world surveillance contexts. Our benchmark provides a practical and comprehensive resource for advancing video-language understanding in safety-critical applications such as intelligent monitoring, incident analysis, and autonomous decision-making.

cs.CV↗

arXiv2025

First Lasing and Stable Operation of a Direct-Amplification Enabled Harmonic Generation Free-Electron laser

Seeded free-electron lasers (FELs) capable of operating at repetition rates up to the MHz level are in high demand for advanced time-resolved spectroscopies, which require both full longitudinal coherence and high average photon flux in the extreme ultraviolet (EUV) and x-ray regimes. However, conventional external-seed laser systems cannot sustain MHz operation with sufficient hundreds of megawatts peak power requirement due to their limited total power. Here, we report the first lasing and stable operation of a direct-amplification-enabled harmonic generation FEL driven by a weak seed laser with MW-level peak power. Beginning with an ultraviolet seed laser with only 0.75 μJ pulse energy, we demonstrate its direct amplification to over 10 μJ within an 8-meter-long modulator. We observe coherent harmonic generation up to the 12th harmonic of the seed and achieve saturation of the 7th harmonic in the radiator. These results represent a crucial milestone toward the realization of MHz-class, fully coherent EUV and x-ray light sources.

physics.acc-ph↗

arXiv2025

Neutron Spin Resonance Near a Lifshitz Transition in Overdoped Ba$_{0.4}$K$_{0.6}$Fe$_2$As$_2$

Elucidating the relationship between spin excitations and fermiology is essential for clarifying the pairing mechanism in iron-based superconductors (FeSCs). Here, we report inelastic neutron scattering results on the hole overdoped Ba$_{0.4}$K$_{0.6}$Fe$_2$As$_2$ near a Lifshitz transition, where the electron pocket at $M$ point is nearly replace by four hole pockets. In the normal state, the spin excitations are observed at incommensurate wave vectors with chimney-like dispersions. By cooling down to the superconducting state, a neutron spin resonance mode emerges with a peak energy of $E_r=$ 14-15 meV weakly modulated along $L$-direction. The incommensurability notably increases at low energies, giving rise to downward dispersions of the resonance mode. This behavior contrasts sharply with the upward dispersions of resonance observed in optimally doped Ba$_{0.67}$K$_{0.33}$Fe$_2$As$_2$ contributed by the hole to electron scattering, but resembles with the cases in KFe$_2$As$_2$ and KCa$_2$Fe$_4$As$_4$F$_2$ where the fermiology are dominated by hole pockets. These results highlight the critical role of electronic structure modifications near the Fermi level, especially in governing interband scattering under imperfect nesting conditions, which fundamentally shape the spin dynamics of FeSCs.

cond-mat.supr-con↗

arXiv2025

AMO Sampler: Enhancing Text Rendering with Overshooting

Achieving precise alignment between textual instructions and generated images in text-to-image generation is a significant challenge, particularly in rendering written text within images. Sate-of-the-art models like Stable Diffusion 3 (SD3), Flux, and AuraFlow still struggle with accurate text depiction, resulting in misspelled or inconsistent text. We introduce a training-free method with minimal computational overhead that significantly enhances text rendering quality. Specifically, we introduce an overshooting sampler for pretrained rectified flow (RF) models, by alternating between over-simulating the learned ordinary differential equation (ODE) and reintroducing noise. Compared to the Euler sampler, the overshooting sampler effectively introduces an extra Langevin dynamics term that can help correct the compounding error from successive Euler steps and therefore improve the text rendering. However, when the overshooting strength is high, we observe over-smoothing artifacts on the generated images. To address this issue, we propose an Attention Modulated Overshooting sampler (AMO), which adaptively controls the strength of overshooting for each image patch according to their attention score with the text content. AMO demonstrates a 32.3% and 35.9% improvement in text rendering accuracy on SD3 and Flux without compromising overall image quality or increasing inference cost. Code available at: https://github.com/hxixixh/amo-release.

cs.CV↗

arXiv2025

Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptation

In federated learning, fine-tuning pre-trained foundation models poses significant challenges, particularly regarding high communication cost and suboptimal model performance due to data heterogeneity between the clients. To address these issues, this paper introduces communication-efficient federated LoRA adaption (CE-LoRA), a method that employs a tri-factorization low-rank adaptation approach with personalized model parameter aggregation. We first presents a novel LoRA parameter factorization by introducing a small-size dense matrix, which can significantly reduce the communication cost and achieve comparable empirical performance than transferring the low-rank parameter matrix used by existing methods. Without violating data privacy, the server considers the client similarity in both training dataset and model parameter space, and learns personalized weights for model aggregation. Our experiments on various LLM and VLM fine-tuning tasks demonstrate that CE-LoRA not only significantly reduces communication overhead but also improves performance under not independently and identically distributed data conditions. In addition, CE-LoRA improves data privacy protection, effectively mitigating gradient-based data reconstruction attacks.

cs.LG↗