Pointer analysis for Rust faces unique challenges arising from its ownership-based memory model and layered abstractions, which complicate how heap-allocated objects flow across functions. Existing k-limited callsite abstractions - designed for earlier languages - are both imprecise and inefficient on large Rust programs. We present Rceus, a Rust-oriented pointer-analysis technique that mitigates points-to set explosion and resource exhaustion caused by cross-function pointer conflation under deep heap encapsulation, a scalability bottleneck that conventional k-limiting cannot address. Rceus performs a fast, coarse-grained pointer-flow pre-analysis to identify precision-critical functions and the essential callsites within their calling contexts. This selective context construction distinguishes parameter-derived flows while avoiding unnecessary expansion. As a result, Rceus cleanly partitions intertwined pointer flows, eliminating context explosion and improving both scalability and precision. On 16 real-world Rust applications, Rceus outperforms state-of-the-art techniques - standard k-limiting, selective k-limiting for Java, and stack-filtered k-limiting for Rust - in both precision and efficiency. The evaluation includes Wasmtime, a WebAssembly runtime with 669K lines of code, where the benefits increase with program size. Rceus also composes with existing techniques, providing a practical and extensible foundation for scalable, precise Rust pointer analysis.

Rust combines high performance with strong memory safety through strict ownership and borrowing rules. However, its unsafe mode reintroduces vulnerabilities by allowing raw-pointer manipulation, a major source of memory-safety bugs. Existing whole-program analyses for Rust often suffer from low recall and high false positives. Since unsafe code is typically small and isolated, we propose a demand-driven alternative. We present Pincer , a flow-, field-, and context-sensitive dataflow analysis framework built on IFDS. Pincer performs mutually bidirectional analysis—backward to trace raw-pointer origins and forward to explore aliases—adapting this strategy to Rust’s ownership model and low-level semantics. On this foundation, Pincer performs a modular, bottom-up vulnerability-oriented typestate analysis to detect use-after-free and double-free bugs. It tracks raw-pointer aliasing and nullness, exploits strong updates at container-manipulating returns, and leverages Rust’s safety invariants to prune provably safe regions via AXM checking. The modular design enables controlled exploration, optionally under a budget, improving scalability. Controlled unsoundness further boosts efficiency while maintaining high recall and precision. We evaluate Pincer on vulnerable programs and large Rust projects. The results show that Pincer detects memory-safety errors more accurately than state-of-the-art analyses while maintaining practical efficiency.

Edge devices face a pressing demand for low-cost object detection networks. However, because of limited computational resources, lightweight detectors often suffer significant performance degradation. In this paper, we propose SFCE-Det, an efficient object detector that achieves remarkable performance with remarkably few parameters and GFLOPs. The key contribution of our work lies in the novel subfeature fusion and cross-layer perceptual enhancement block (SFCE-Block), which effectively extracts feature information from images at a very low computational cost. SFCE-Block can be seamlessly integrated into existing convolutional neural networks and serves as a plug-and-play component for lightweight upgrades to the network. SFCE-Block can not only be used to upgrade classic models but also has excellent lightweight effects on state-of-the-art models (e.g. YoLOv8). Additionally, we propose a dynamic label assignment strategy that leverages global label correlation to further enhance the performance of SFCE-Det. Experimental results demonstrate that SFCE-Det surpasses many state-of-the-art lightweight object detectors, on multiple public datasets while maintaining an extremely low cost. For example, SFCE-Det-D2 achieves an impressive mAP of 83.4% on the PASCAL VOC dataset, comparable to YOLOv8-S. However, SFCE-Det-D2 requires only 26% of the parameters and 35% of the GFLOPs, which are 2.96M parameters and 9.9 GFLOPs, respectively.

Plant sensing plays an important role in modern smart agriculture and the farming industry. Remote radio sensing allows for monitoring essential indicators of plant health, such as leaf water content (WC). While recent studies have shown the potential of using millimeter-wave (mmWave) radar for plant sensing, many overlook crucial factors, such as leaf structure and surface roughness, which can impact the accuracy of the measurements. In this article, we introduce Leafeon, which leverages mmWave radar to measure leaf WC noninvasively. Utilizing electronic beam steering, multiple leaf perspectives are sent to a custom deep neural network, which discerns unique reflection patterns from subtle antenna variations, ensuring accurate and robust leaf WC estimations. We implement a prototype of Leafeon using a Commercial Off-The-Shelf mmWave radar and evaluate its performance with a variety of different leaf types. Leafeon was trained in-lab using high-resolution destructive leaf measurements, achieving a mean absolute error (MAE) of leaf WC as low as 3.17% for the Avocado leaf, significantly outperforming the state-of-the-art approaches with an MAE reduction of up to 55.7%. Furthermore, we conducted experiments on live plants in both indoor and glasshouse experimental farm environments. Our results showed a strong correlation between predicted leaf WC levels and drought events.