EECD-Net: Energy-Efficient Crack Detection with Spiking Neural Networks and Gated Attention

Crack detection on road surfaces is a critical measurement technology in the instrumentation domain, essential for ensuring infrastructure safety and transportation reliability. However, due to limited energy and low-resolution imaging, smart terminal devices struggle to maintain real-time monitoring performance. To overcome these challenges, this paper proposes a multi-stage detection approach for road crack detection, EECD-Net, to enhance accuracy and energy efficiency of instrumentation. Specifically, the sophisticated Super-Resolution Convolutional Neural Network (SRCNN) is employed to address the inherent challenges of low-quality images, which effectively enhance image resolution while preserving critical structural details. Meanwhile, a Spike Convolution Unit (SCU) with Continuous Integrate-and-Fire (CIF) neurons is proposed to convert these images into sparse pulse sequences, significantly reducing power consumption. Additionally, a Gated Attention Transformer (GAT) module is designed to strategically fuse multi-scale feature representations through adaptive attention mechanisms, effectively capturing both long-range dependencies and intricate local crack patterns, and significantly enhancing detection robustness across varying crack morphologies. The experiments on the CrackVision12K benchmark demonstrate that EECD-Net achieves a remarkable 98.6\% detection accuracy, surpassing state-of-the-art counterparts such as Hybrid-Segmentor by a significant 1.5\%. Notably, the EECD-Net maintains exceptional energy efficiency, consuming merely 5.6 mJ, which is a substantial 33\% reduction compared to baseline implementations. This work pioneers a transformative approach in instrumentation-based crack detection, offering a scalable, low-power solution for real-time, large-scale infrastructure monitoring in resource-constrained environments.

MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm

We introduce MonkeyOCR, a vision-language model for document parsing that advances the state of the art by leveraging a Structure-Recognition-Relation (SRR) triplet paradigm. This design simplifies what would otherwise be a complex multi-tool pipeline (as in MinerU's modular approach) and avoids the inefficiencies of processing full pages with giant end-to-end models (e.g., large multimodal LLMs like Qwen-VL). In SRR, document parsing is abstracted into three fundamental questions - "Where is it?" (structure), "What is it?" (recognition), and "How is it organized?" (relation) - corresponding to layout analysis, content identification, and logical ordering. This focused decomposition balances accuracy and speed: it enables efficient, scalable processing without sacrificing precision. To train and evaluate this approach, we introduce the MonkeyDoc (the most comprehensive document parsing dataset to date), with 3.9 million instances spanning over ten document types in both Chinese and English. Experiments show that MonkeyOCR outperforms MinerU by an average of 5.1%, with particularly notable improvements on challenging content such as formulas (+15.0%) and tables (+8.6%). Remarkably, our 3B-parameter model surpasses much larger and top-performing models, including Qwen2.5-VL (72B) and Gemini 2.5 Pro, achieving state-of-the-art average performance on English document parsing tasks. In addition, MonkeyOCR processes multi-page documents significantly faster (0.84 pages per second compared to 0.65 for MinerU and 0.12 for Qwen2.5-VL-7B). The 3B model can be efficiently deployed for inference on a single NVIDIA 3090 GPU. Code and models will be released at https://github.com/Yuliang-Liu/MonkeyOCR.

RepoMaster: Autonomous Exploration and Understanding of GitHub Repositories for Complex Task Solving

The ultimate goal of code agents is to solve complex tasks autonomously. Although large language models (LLMs) have made substantial progress in code generation, real-world tasks typically demand full-fledged code repositories rather than simple scripts. Building such repositories from scratch remains a major challenge. Fortunately, GitHub hosts a vast, evolving collection of open-source repositories, which developers frequently reuse as modular components for complex tasks. Yet, existing frameworks like OpenHands and SWE-Agent still struggle to effectively leverage these valuable resources. Relying solely on README files provides insufficient guidance, and deeper exploration reveals two core obstacles: overwhelming information and tangled dependencies of repositories, both constrained by the limited context windows of current LLMs. To tackle these issues, we propose RepoMaster, an autonomous agent framework designed to explore and reuse GitHub repositories for solving complex tasks. For efficient understanding, RepoMaster constructs function-call graphs, module-dependency graphs, and hierarchical code trees to identify essential components, providing only identified core elements to the LLMs rather than the entire repository. During autonomous execution, it progressively explores related components using our exploration tools and prunes information to optimize context usage. Evaluated on the adjusted MLE-bench, RepoMaster achieves a 110% relative boost in valid submissions over the strongest baseline OpenHands. On our newly released GitTaskBench, RepoMaster lifts the task-pass rate from 24.1% to 62.9% while reducing token usage by 95%. Our code and demonstration materials are publicly available at https://github.com/wanghuacan/RepoMaster.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition

Multi-task learning (MTL) enables the efficient transfer of extra knowledge acquired from other tasks. The high correlation between multimodal sentiment analysis (MSA) and multimodal emotion recognition (MER) supports their joint training. However, existing methods primarily employ hard parameter sharing, ignoring parameter conflicts caused by complex task correlations. In this paper, we present a novel MTL method for MSA and MER, termed Multimodal Mixture of Low-Rank Experts (MMoLRE). MMoLRE utilizes shared and task-specific experts to distinctly model common and unique task characteristics, thereby avoiding parameter conflicts. Additionally, inspired by low-rank structures in the Mixture of Experts (MoE) framework, we design low-rank expert networks to reduce parameter and computational overhead as the number of experts increases. Extensive experiments on the CMU-MOSI and CMU-MOSEI benchmarks demonstrate that MMoLRE achieves state-of-the-art performance on the MSA task and competitive results on the MER task.

HGFormer: Topology-Aware Vision Transformer with HyperGraph Learning

The computer vision community has witnessed an extensive exploration of vision transformers in the past two years. Drawing inspiration from traditional schemes, numerous works focus on introducing vision-specific inductive biases. However, the implicit modeling of permutation invariance and fully-connected interaction with individual tokens disrupts the regional context and spatial topology, further hindering higher-order modeling. This deviates from the principle of perceptual organization that emphasizes the local groups and overall topology of visual elements. Thus, we introduce the concept of hypergraph for perceptual exploration. Specifically, we propose a topology-aware vision transformer called HyperGraph Transformer (HGFormer). Firstly, we present a Center Sampling K-Nearest Neighbors (CS-KNN) algorithm for semantic guidance during hypergraph construction. Secondly, we present a topology-aware HyperGraph Attention (HGA) mechanism that integrates hypergraph topology as perceptual indications to guide the aggregation of global and unbiased information during hypergraph messaging. Using HGFormer as visual backbone, we develop an effective and unitive representation, achieving distinct and detailed scene depictions. Empirical experiments show that the proposed HGFormer achieves competitive performance compared to the recent SoTA counterparts on various visual benchmarks. Extensive ablation and visualization studies provide comprehensive explanations of our ideas and contributions.

PromptMID: Modal Invariant Descriptors Based on Diffusion and Vision Foundation Models for Optical-SAR Image Matching

The ideal goal of image matching is to achieve stable and efficient performance in unseen domains. However, many existing learning-based optical-SAR image matching methods, despite their effectiveness in specific scenarios, exhibit limited generalization and struggle to adapt to practical applications. Repeatedly training or fine-tuning matching models to address domain differences is not only not elegant enough but also introduces additional computational overhead and data production costs. In recent years, general foundation models have shown great potential for enhancing generalization. However, the disparity in visual domains between natural and remote sensing images poses challenges for their direct application. Therefore, effectively leveraging foundation models to improve the generalization of optical-SAR image matching remains challenge. To address the above challenges, we propose PromptMID, a novel approach that constructs modality-invariant descriptors using text prompts based on land use classification as priors information for optical and SAR image matching. PromptMID extracts multi-scale modality-invariant features by leveraging pre-trained diffusion models and visual foundation models (VFMs), while specially designed feature aggregation modules effectively fuse features across different granularities. Extensive experiments on optical-SAR image datasets from four diverse regions demonstrate that PromptMID outperforms state-of-the-art matching methods, achieving superior results in both seen and unseen domains and exhibiting strong cross-domain generalization capabilities. The source code will be made publicly available https://github.com/HanNieWHU/PromptMID.

MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge Translation

Smart contracts, closely intertwined with cryptocurrency transactions, have sparked widespread concerns about considerable financial losses of security issues. To counteract this, a variety of tools have been developed to identify vulnerability in smart contract. However, they fail to overcome two challenges at the same time when faced with smart contract bytecode: (i) strong interference caused by enormous non-relevant instructions; (ii) missing semantics of bytecode due to incomplete data and control flow dependencies. In this paper, we propose a multi-teacher based bytecode vulnerability detection method, namely Multi-Teacher Vulnerability Hunter (MTVHunter), which delivers effective denoising and missing semantic to bytecode under multi-teacher guidance. Specifically, we first propose an instruction denoising teacher to eliminate noise interference by abstract vulnerability pattern and further reflect in contract embeddings. Secondly, we design a novel semantic complementary teacher with neuron distillation, which effectively extracts necessary semantic from source code to replenish the bytecode. Particularly, the proposed neuron distillation accelerate this semantic filling by turning the knowledge transition into a regression task. We conduct experiments on 229,178 real-world smart contracts that concerns four types of common vulnerabilities. Extensive experiments show MTVHunter achieves significantly performance gains over state-of-the-art approaches.

OCORD: Open-Campus Object Removal Dataset

The rapid advancements in generative models, particularly diffusion-based techniques, have revolutionized image inpainting tasks by enabling the generation of high-fidelity and diverse content. However, object removal remains under-explored as a specific subset of inpainting, facing challenges such as inadequate semantic understanding and the unintended generation of artifacts. Existing datasets for object removal often rely on synthetic data, which fails to align with real-world scenarios, limiting model performance. Although some real-world datasets address these issues partially, they suffer from scalability, annotation inefficiencies, and limited realism in physical phenomena such as lighting and shadows. To address these limitations, this paper introduces a novel approach to object removal by constructing a high-resolution real-world dataset through long-duration video capture with fixed camera settings. Leveraging advanced tools such as Grounding-DINO, Segment-Anything-Model, and MASA for automated annotation, we provides image, background, and mask pairs while significantly reducing annotation time and labor. With our efficient annotation pipeline, we release the first fully open, high-resolution real-world dataset for object removal, and improved performance in object removal tasks through fine-tuning of pre-trained diffusion models.

Channel Modeling and Rate Analysis of Optical Inter-Satellite Link (OISL)

Optical inter-satellite links (OISLs) improve connectivity between satellites in space. They offer advantages such as high-throughput data transfer and reduced size, weight, and power requirements compared to traditional radio frequency transmission. However, the channel model and communication performance for long-distance inter-satellite laser transmission still require in-depth study. In this paper, we first develop a channel model for OISL communication within non-terrestrial networks (NTN) by accounting for pointing errors caused by satellite jitter and tracking noise. We derive the distributions of the channel state arising from these pointing errors and calculate their average value. Additionally, we determine the average achievable data rate for OISL communication in NTN and design a cooperative OISL system, highlighting a trade-off between concentrating beam energy and balancing misalignment. We calculate the minimum number of satellites required in cooperative OISLs to achieve a targeted data transmission size while adhering to latency constraints. This involves exploring the balance between the increased data rate of each link and the cumulative latency across all links. Finally, simulation results validate the effectiveness of the proposed analytical model and provide insights into the optimal number of satellites needed for cooperative OISLs and the optimal laser frequency to use.

AI-Powered Algorithm-Centric Quantum Processor Topology Design

Quantum computing promises to revolutionize various fields, yet the execution of quantum programs necessitates an effective compilation process. This involves strategically mapping quantum circuits onto the physical qubits of a quantum processor. The qubits' arrangement, or topology, is pivotal to the circuit's performance, a factor that often defies traditional heuristic or manual optimization methods due to its complexity. In this study, we introduce a novel approach leveraging reinforcement learning to dynamically tailor qubit topologies to the unique specifications of individual quantum circuits, guiding algorithm-driven quantum processor topology design for reducing the depth of mapped circuit, which is particularly critical for the output accuracy on noisy quantum processors. Our method marks a significant departure from previous methods that have been constrained to mapping circuits onto a fixed processor topology. Experiments demonstrate that we have achieved notable enhancements in circuit performance, with a minimum of 20\% reduction in circuit depth in 60\% of the cases examined, and a maximum enhancement of up to 46\%. Furthermore, the pronounced benefits of our approach in reducing circuit depth become increasingly evident as the scale of the quantum circuits increases, exhibiting the scalability of our method in terms of problem size. This work advances the co-design of quantum processor architecture and algorithm mapping, offering a promising avenue for future research and development in the field.