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Large deployable mesh reflectors play a critical role in satellite communications, Earth observation, and deep-space exploration, offering high-gain antenna performance through precisely shaped reflective surfaces. Traditional dynamic modeling approaches—such as wave-based and finite element methods—often struggle to accurately capture the complex behavior of three-dimensional reflectors due to oversimplifications of cable members. To address these challenges, this paper proposes a novel spatial discretization framework that systematically decomposes cable member displacements into boundary-induced and internal components in a global Cartesian coordinate system. The framework derives a system of ordinary differential equations for each cable member by enforcing the Lagrange’s equations, capturing both longitudinal and transverse internal displacement of the cable member. Numerical simulations of a two-dimensional cable-network structure and a center-feed parabolic deployable mesh reflector with 101 nodes illustrate the improved accuracy of the proposed method in predicting vibration characteristics across a broad frequency range. Compared to standard finite element analysis, the proposed method more effectively identifies both low- and high-frequency modes and offers robust convergence and accurate prediction for both frequency and transient responses of the structure. This enhanced predictive capability underscores the significance of incorporating internal cable member displacements for reliable dynamic modeling of large deployable mesh reflectors, ultimately informing better design, control, and on-orbit performance of future space-based reflector systems. 

Diffusion policies have achieved superior performance in imitation learning and offline reinforcement learning (RL) due to their rich expressiveness. However, the conventional diffusion training procedure requires samples from target distribution, which is impossible in online RL since we cannot sample from the optimal policy. Backpropagating policy gradient through the diffusion process incurs huge computational costs and instability, thus being expensive and not scalable. To enable efficient training of diffusion policies in online RL, we generalize the conventional denoising score matching by reweighting the loss function. The resulting Reweighted Score Matching (RSM) preserves the optimal solution and low computational cost of denoising score matching, while eliminating the need to sample from the target distribution and allowing learning to optimize value functions. We introduce two tractable reweighted loss functions to solve two commonly used policy optimization problems, policy mirror descent and max-entropy policy, resulting in two practical algorithms named Diffusion Policy Mirror Descent (DPMD) and Soft Diffusion Actor-Critic (SDAC). We conducted comprehensive comparisons on MuJoCo benchmarks. The empirical results show that the proposed algorithms outperform recent diffusion-policy online RLs on most tasks, and the DPMD improves more than 120% over soft actor-critic on Humanoid and Ant. 

Abstract Sulfate-proton co-transporters (SULTRs) mediate sulfate uptake, transport, storage, and assimilation in plants. The SULTR family has historically been classified into four groups (SULTR1-SULTR4), with well-characterized roles for SULTR groups 1, 2, and 4. However, the functions of the large and diverse SULTR3 group remain poorly understood. Here, we present an updated phylogenetic analysis of SULTRs across angiosperms, including multiple early-divergent lineages. Our results suggest that the enigmatic SULTR3 group comprises four distinct subfamilies that predate the emergence of angiosperms, providing a basis for reclassifying the SULTR family into seven subfamilies. This expanded classification is supported by subfamily-specific gene structures and amino acid substitutions in the substrate-binding pocket. Structural modeling identified three serine residues uniquely lining the substrate-binding pocket of SULTR3.4, enabling three hydrogen bonds with the phosphate ion. The data support the proposed neofunctionalization of this subfamily for phosphate allocation within vascular tissues. Transcriptome analysis of Populus tremula × alba revealed divergent tissue expression preferences among SULTR subfamilies and between genome duplicates. We observed partitioned expression in vascular tissues among the four SULTR3 subfamilies, with PtaSULTR3.4a and PtaSULTR3.2a preferentially expressed in primary and secondary xylem, respectively. Gene coexpression analysis revealed coordinated expression of PtaSULTR3.4a with genes involved in phosphate starvation responses and nutrient transport, consistent with a potential role in phosphate homeostasis. In contrast, PtaSULTR3.2a was strongly coexpressed with lignification and one-carbon metabolism genes and their upstream transcription regulators. PtaSULTR3.2a belongs to a eudicot-specific branch of the SULTR3.1 subfamily found only in perennial species, suggesting a specialized role in lignifying tissues. Together, our findings provide a refined phylogenetic framework for the SULTR family and suggest that the expanded SULTR3 subfamilies have undergone neofunctionalization during the evolution of vascular and perennial plants. 

The pursuit of robustness has recently been a popular topic in reinforcement learning (RL) research, yet the existing methods generally suffer from computation issues that obstruct their real-world implementation. In this paper, we consider MDPs with low-rank structures, where the transition kernel can be written as a linear product of feature map and factors. We introduce *duple perturbation* robustness, i.e. perturbation on both the feature map and the factors, via a novel characterization of (𝜉,𝜂) -ambiguity sets featuring computational efficiency. Our novel low-rank robust MDP formulation is compatible with the low-rank function representation view, and therefore, is naturally applicable to practical RL problems with large or even continuous state-action spaces. Meanwhile, it also gives rise to a provably efficient and practical algorithm with theoretical convergence rate guarantee. Lastly, the robustness of our proposed approach is justified by numerical experiments, including classical control tasks with continuous state-action spaces. 

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.