The task of issue resolving aims to modify a codebase to generate a patch that addresses a given issue. However, most existing benchmarks focus almost exclusively on Python, making them insufficient for evaluating Large Language Models (LLMs) across different programming languages. To bridge this gap, we introduce a multilingual issue-resolving benchmark, called Multi-SWE-bench, covering 8 languages of Python, Java, TypeScript, JavaScript, Go, Rust, C, and C++. In particular, this benchmark includes a total of 2,132 high-quality instances, carefully curated by 68 expert annotators, ensuring a reliable and accurate evaluation of LLMs on the issue-resolving task. Based on human-annotated results, the issues are further classified into three difficulty levels. We evaluate a series of state-of-the-art models on Multi-SWE-bench, utilizing both procedural and agent-based frameworks for issue resolving. Our experiments reveal three key findings: (1) Limited generalization across languages: While existing LLMs perform well on Python issues, their ability to generalize across other languages remains limited; (2) Performance aligned with human-annotated difficulty: LLM-based agents' performance closely aligns with human-assigned difficulty, with resolution rates decreasing as issue complexity rises; and (3) Performance drop on cross-file issues: The performance of current methods significantly deteriorates when handling cross-file issues. These findings highlight the limitations of current LLMs and underscore the need for more robust models capable of handling a broader range of programming languages and complex issue scenarios.

The objective of visible-infrared person re-identification is to accurately match pedestrian images captured in different modalities. Since these images are taken from varying viewpoints by different cameras, the cross-modal detection task must address both modality discrepancies and camera variations. Many existing approaches primarily focus on minimizing inter-modality differences to enhance retrieval accuracy, often overlooking the impact of camera viewpoint differences. To tackle these challenges, this article introduces a hierarchical feature decoupling network. First, the network decouples and extracts camera-related and camera-irrelated features separately to mitigate the effects of camera variations. Second, it addresses modality differences by extracting modality-independent features. Additionally, an adversarial decoupling loss is employed to further disentangle identity-irrelevant information from identity-relevant features, thereby boosting the system’s accuracy and robustness. Extensive experiments conducted on the SYSU-MM01 and RegDB datasets validate the effectiveness of the proposed method.

Radio frequency identification (RFID) is a key technology to realize IoT (Internet of Things) dreams. RFID technology has been emerging in sensing, identification, tracking, and localization of goods. In order to tag a huge number of things, it is cost‐effective to use one RFID antenna for tagging different things. Therefore, in this paper a platform tolerant RFID tag antenna with tunable capability is proposed. The proposed tag antenna is designed and optimized using characteristic mode analysis (CMA). Moreover, this tag antenna consists of a folded patch wrapped around FR 4 substrate and a feeding loop element printed on a paper substrate. The inductive feeding loop is stacked over folded patch and it provides impedance match with RFID chip. Because of separate radiating and feeding element, this tag antenna has a versatility of impedance matching with any RFID chip. Furthermore, this tag is able to cover American RFID band (902–928 MHz) and can be tuned to European RFID band (865–868 MHz) by adding tunable strips. In order to demonstrate platform tolerant operation, the read range of RFID tag is measured by mounting it on different materials. The maximum read range of RFID tag is 4.5 m in free space or on dielectrics and 6.5 m above 200 × 200 mm2 metal plate, respectively.