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In this paper, we propose a linear second-order numerical method for solving the Allen-Cahn equation with general mobility. The fully-discrete scheme is achieved by using the Crank-Nicolson formula for temporal integration and the central difference method for spatial approximation, together with two additional stabilization terms. Under mild constraints on the two stabilizing parameters, the proposed numerical scheme is shown to unconditionally preserve the discrete maximum bound principle and the discrete original energy dissipation law. Error estimate in the 𝐿∞ norm is successfully derived for the proposed scheme. Finally, some numerical experiments are conducted to verify the theoretical results and demonstrate the performance of the proposed scheme in combination with an adaptive time-stepping strategy. 

In software development, many documents (e.g., tutorials for tools and mobile application websites) contain screenshots of graphical user interfaces (GUIs) to illustrate functionalities. Although screenshots are critical in such documents, screenshots can become outdated, especially if document developers forget to update them. Outdated screenshots can mislead users and diminish the credibility of documentation. Identifying screenshots manually is tedious and error-prone, especially when documents are numerous. However, no existing tools are proposed to detect outdated screenshots in GUI documents. To mitigate manual efforts, we propose DOSUD, a novel approach for detecting outdated screenshots. It is challenging to identify outdated screenshots since the differences are subtle and only specific areas are useful to identify such screenshots. To address the challenges, DOSUD automatically extracts and labels screenshots and trains a classification model to identify outdated screenshots. As the first exploration, we focus on Android applications and the most popular IDE, VS Code. We evaluated DOSUD on a benchmark comprising 10 popular applications, achieving high F1-scores. When applied in the wild, DOSUD identified 20 outdated screenshots across 50 Android application websites and 17 outdated screenshots in VS Code documentation. VS Code developers have confirmed and fixed all our bug reports. 

Open-text responses provide researchers and educators with rich, nuanced insights that multiple-choice questions cannot capture. When reliably assessed, such responses have the potential to enhance teaching and learning. However, scaling and consistently capturing these nuances remain significant challenges, limiting the widespread use of open-text questions in educational research and assessments. In this paper, we introduce and evaluate GradeOpt, a unified multiagent automatic short-answer grading (ASAG) framework that leverages large language models (LLMs) as graders for short-answer responses. More importantly, GradeOpt incorporates two additional LLM-based agents—the reflector and the refiner—into the multi-agent system. This enables GradeOpt to automatically optimize the original grading guidelines by performing self-reflection on its errors. To assess GradeOpt's effectiveness, we conducted experiments on two representative ASAG datasets, which include items designed to capture key aspects of teachers' pedagogical knowledge and students' learning progress. Our results demonstrate that GradeOpt consistently outperforms representative baselines in both grading accuracy and alignment with human evaluators across different knowledge domains. Finally, comprehensive ablation studies validate the contributions of GradeOpt's individual components, confirming their impact on overall performance. 

Abstract In plants, embryo size is determined via interactions between metabolic and developmental signals. Maize (Zea mays) big embryo 6 (bige6) enhances embryo size while sharply reducing plant growth. Here, we show that BigE6 encodes a plastidial prephenate aminotransferase (PPA-AT), a key enzyme in the arogenate pathway for L-phenylalanine (Phe) and L-tyrosine (Tyr) biosynthesis. The maize BigE6 paralog, BigE6Like, encodes a cytosol-localized PPA-AT, revealing Phe and Tyr biosynthesis via cytosolic arogenate as a potential alternative to the known cytosolic phenylpyruvate pathway. Moreover, the single PPA-AT gene of Arabidopsis (Arabidopsis thaliana) encodes plastidial and cytosolic enzymes by alternative splicing. Transgenic rescue of a ppa-at mutant in Arabidopsis demonstrates that the plastidial PPA-AT is indispensable for seed formation due, in part, to its essential role in the female gametophyte. Leaves of bige6 maize maintained overall homeostasis for aromatic amino acids and downstream metabolites, revealing a resilience of mechanisms that scale growth to a limiting supply of Phe and Tyr. In bige6 seeds, broad perturbation of amino acid homeostasis is associated with transcriptomic upregulation of growth processes in the embryo and endosperm, implicating amino acid signaling in the regulation of embryo size. Our findings reveal the complexity and developmental dependence of growth responses to limiting amino acid biosynthesis. 

As widely used in data-driven decision-making, recommender systems have been recognized for their capabilities to provide users with personalized services in many user-oriented online services, such as E-commerce (e.g., Amazon, Taobao, etc.) and Social Media sites (e.g., Facebook and Twitter). Recent works have shown that deep neural networks-based recommender systems are highly vulnerable to adversarial attacks, where adversaries can inject carefully crafted fake user profiles (i.e., a set of items that fake users have interacted with) into a target recommender system to promote or demote a set of target items. Instead of generating users with fake profiles from scratch, in this paper, we introduce a novel strategy to obtain “fake” user profiles via copying cross-domain user profiles, where a reinforcement learning-based black-box attacking framework (CopyAttack+) is developed to effectively and efficiently select cross-domain user profiles from the source domain to attack the target system. Moreover, we propose to train a local surrogate system for mimicking adversarial black-box attacks in the source domain, so as to provide transferable signals with the purpose of enhancing the attacking strategy in the target black-box recommender system. Comprehensive experiments on three real-world datasets are conducted to demonstrate the effectiveness of the proposed attacking framework.