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Grinding and hydrogen-annealing activate ANi₂P₄(A = Ba or Sr) clathrates toward the reduction of nitrate or nitroarenes. Activity and selectivity can be tuned based on the catalyst activation method, particle size, or acid used. 

A query algorithm based on homomorphism counts is a procedure for determining whether a given instance satisfies a property by counting homomorphisms between the given instance and finitely many predetermined instances. In a left query algorithm, we count homomorphisms from the predetermined instances to the given instance, while in a right query algorithm we count homomorphisms from the given instance to the predetermined instances. Homomorphisms are usually counted over the semiring ℕ of non-negative integers; it is also meaningful, however, to count homomorphisms over the Boolean semiring 𝔹, in which case the homomorphism count indicates whether or not a homomorphism exists. We first characterize the properties that admit a left query algorithm over 𝔹 by showing that these are precisely the properties that are both first-order definable and closed under homomorphic equivalence. After this, we turn attention to a comparison between left query algorithms over 𝔹 and left query algorithms over ℕ. In general, there are properties that admit a left query algorithm over ℕ but not over 𝔹. The main result of this paper asserts that if a property is closed under homomorphic equivalence, then that property admits a left query algorithm over 𝔹 if and only if it admits a left query algorithm over ℕ. In other words and rather surprisingly, homomorphism counts over ℕ do not help as regards properties that are closed under homomorphic equivalence. Finally, we characterize the properties that admit both a left query algorithm over 𝔹 and a right query algorithm over 𝔹. 

Query optimizers are a performance-critical component in every database system. Due to their complexity, optimizers take experts months to write and years to refine. In this work, we demonstrate for the first time that learning to optimize queries without learning from an expert optimizer is both possible and efficient. We present Balsa, a query optimizer built by deep reinforcement learning. Balsa first learns basic knowledge from a simple, environment-agnostic simulator, followed by safe learning in real execution. On the Join Order Benchmark, Balsa matches the performance of two expert query optimizers, both open-source and commercial, with two hours of learning, and outperforms them by up to 2.8× in workload runtime after a few more hours. Balsa thus opens the possibility of automatically learning to optimize in future compute environments where expert-designed optimizers do not exist. 

Abstract As a unique nonlinear optical material, Ba₃(ZnB₅O₁₀)PO₄(BZBP) boasts a range of distinctive properties, including low anisotropic thermal expansivity, high specific heat, minimal walk‐off effect, large acceptance angle, non‐hygroscopicity, and high conversion efficiency. These features position BZBP as a highly promising candidate for crucial components in ultraviolet (UV) laser systems. Notably, all previous studies have been conducted under ambient pressures. In this research, synchrotron X‐ray diffraction and Raman spectroscopy are employed to investigate BZBP's behavior under extreme conditions. The findings revealed that BZBP remains exceptionally stable up to 43 Gigapascals (GPa), significantly extending its application range from ambient to high‐pressure environments. This stability enhancement opens new avenues for utilizing BZBP in optical systems designed to function under extreme conditions. Additionally, the study determined BZBP's bulk modulus (110 GPa) and linear compressibility along each lattice axis. Theoretical computations are used to assign the Raman modes, characterize their corresponding lattice vibrations, validate the experimental results, and elucidate the mechanisms underlying the material's remarkable stability.