The continuous advancement of remote sensing technology has enabled high-resolution Earth observation; however, interpreting these expansive images using modern Vision Foundation Models (VFMs) remains a significant challenge. Unlike object-centric natural images, remote sensing imagery is fundamentally characterized by extreme target sparsity and massive spatial redundancy. Key objects of interest (e.g., ships, vehicles, scattered buildings) often occupy less than 1% of the spatial extent, surrounded by vast, target-free backgrounds. Many existing VFMs rely on uniform dense processing (e.g., Vision Transformers), while a substantial branch adopts pixel-reconstruction pre-training paradigms (e.g., Masked Autoencoders). These designs may waste substantial computational capacity on modeling redundant backgrounds and dilute the feature representations of small, sparse targets. To bridge this structural misalignment, we propose DynamicVis, a remote sensing visual foundation model explicitly tailored to spatial sparsity in high-resolution imagery, with a focus on large-scale visual pre-training and transferable spatial representations for diverse downstream perception tasks. Architecturally, DynamicVis introduces a Dynamic Region-Aware State Space Model (SSM) that bypasses uniform computation. It adaptively routes and incrementally models only task-relevant, high-salience tokens while employing a parameter-free integration for background context, drastically reducing the complexity of processing ultra-long 2D token sequences (∼100,000). Crucially, to equip the network with robust spatial-selection capabilities, we propose a novel weakly supervised Region-Level Meta-Embedding Multi-Instance Learning (MIL) pre-training paradigm. Trained on a million-scale dataset, this paradigm promotes foreground-aware regional discrimination in the latent semantic space, mitigating the semantic ambiguity of conventional pixel-reconstruction methods. Extensive evaluations across nine diverse downstream visual perception tasks reveal that DynamicVis achieves competitive performance across region-, instance-, and pixel-level remote sensing tasks, with particularly strong results in sparse-target and instance-level perception tasks (e.g., small object detection and instance segmentation). Notably, DynamicVis processes high-resolution imagery (2048 × 2048) with merely 97 ms latency and 833 MB of memory consumption without employing any acceleration techniques (approximately 6% and 3% of a base ViT, respectively), offering a favorable trade-off between computational scalability and representation accuracy for multi-granular remote sensing analysis. Source code is available at https://github.com/KyanChen/DynamicVis.
Existing legal benchmarks for Large Language Models (LLMs) primarily focus on final judgment outcomes or isolated subtasks, making it difficult to systematically evaluate the multi-step reasoning processes and reasoning quality required in real-world legal decision-making. To address this gap, we introduce MSLR, a Chinese benchmark for multi-step legal reasoning, which models and evaluates complete judicial reasoning trajectories based on the IRAC (Issue-Rule-Application-Conclusion) framework. MSLR comprises 1389 Chinese insider trading judgments issued between 2005 and 2024, with approximately 60,000 step-level reasoning paths annotated through a combination of human and automated methods, averaging 43 intermediate reasoning steps per case. Based on MSLR, we adopt two evaluation metrics, IRAC Recall and LLM Score, to provide a fine-grained assessment of legal reasoning capabilities. We systematically evaluate 19 mainstream general-purpose, legal-domain, and reasoning-oriented models. The results show that current models exhibit overall limited performance on multi-step legal reasoning tasks; even the best-performing o1-mini model fails to exceed 75% IRAC Recall. In addition, we report the performance of mainstream models on structured annotation tasks and legal judgment reasoning tasks. The MSLR dataset and benchmark construction code are publicly available at https://github.com/yuwenhan07/MSLR-Bench.
Due to their widespread use in industry, several techniques have been proposed in the literature to fuzz REST APIs. Existing fuzzers for REST APIs have been focusing on detecting crashes (e.g., 500 HTTP server error status code). However, security vulnerabilities can have major drastic consequences on existing cloud infrastructures.In this paper, we propose a series of novel automated oracles aimed at detecting violations of access policies in REST APIs, as well as executing traditional attacks such as SQL Injection and XSS. These novel automated oracles can be integrated into existing fuzzers, in which, once the fuzzing session is completed, a “security testing” phase is executed to verify these oracles. When a security fault is detected, as output our technique is able to generate executable test cases in different formats, like Java, Kotlin, Python and JavaScript test suites.Our novel techniques are integrated as an extension of EvoMaster, a state-of-the-art open-source fuzzer for REST APIs. Experiments are carried out on 9 artificial examples, 8 vulnerable-by-design REST APIs with black-box testing, and 36 REST APIs from the WFD corpus with white-box testing, for a total of 52 distinct APIs. Results show that our novel oracles and their automated integration in a fuzzing process can lead to detecting security issues in several of these APIs.
Metallic glasses have attracted extensive interest for outstanding properties, yet their intrinsic atomic disorder makes precise composition design highly challenging. In this work, we develop a suite of computational methods to predict the key properties of metallic glasses and to accelerate their composition development. A machine learning interatomic potential for metallic glasses is established using a dataset of 20,400 configurations from 19 representative binary and ternary systems. The model demonstrates excellent predictive performance on an independent test set, with a mean absolute error of 5.06 meV/atom for energy, 128.51 meV/A & ring; for force, and 0.17 GPa for stress. Based on this model, automated simulation workflows are further developed for density, Young's modulus, and glass transition temperature, enabling quantitative high-throughput property mapping. The calculated trends are in good agreement with the experimental results. As a representative validation case, Co-Ir-Ta-B metallic glasses are investigated. The simulations show that the decrease in Young's modulus is associated with a structurally loosened atomic packing state, and the largely preserved coordination environment supports the validity of the similarelement substitution strategy. Overall, the proposed framework provides a screening-oriented computational route for quantitative property mapping and offers atomistic structural evidence to rationalize experimentally observed property trends. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
Ground Delay Programs (GDPs) are widely used to mitigate demand-capacity imbalances in air traffic operations. However, existing studies typically address GDP planning from either the perspective of air traffic control or that of individual airlines, with limited attention to their coordination. This study develops a collaborative framework for the airline-driven GDP that integrates proactive slot reduction and airline precancellation. The problem is formulated as a two-stage stochastic program. The first determines slot reductions and flight precancellations in anticipation of various potential capacity scenarios, whereas the second adjusts slot allocations and executes airline recovery actions based on the realized slot capacity. To address the computational challenges of this large-scale problem, we propose an efficient Lagrangian dual decomposition algorithm, which reduces the computation time by 49.06% on average compared with the standard column generation approach. Numerical experiments based on real-world data show that the proposed framework consistently achieves lower expected total recovery costs than rule-based deterministic sequential strategies commonly used in current GDP practice. Moreover, when fairness constraints are imposed, allowing slot exchanges among airlines further reduces total costs by an average of 1.37%. These findings highlight the importance of collaborative decision-making between air traffic managers and airlines, providing a robust and practical tool to enhance system-wide efficiency under capacity disruptions.
