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低空新型基础设施是低空经济高质量发展的核心支撑，其价值闭环构建对促进产业可持续发展具有重要意义。本文分析了EPC+O、投建营一体化、政企合作、特许经营和LATOD等商业模式创新的价值闭环途径，探讨空域管理改革、数据要素市场化配置和产业基金等政策支持体系如何降低运营成本、提升价值创造效率，研究通信导航监视反制、“一塔一城”和设施/人员复用等技术路径如何支撑价值闭环实现。研究表明，低空新型基础设施的价值闭环构建需要商业模式创新、政策支持机制和技术应用路径的协同演进，通过“公益底座+市场服务”的组合模式，构建“建设-运营-价值创造-再投资”的良性循环。

Sparse Autoencoders (SAEs) are widely used for mechanistic interpretability in large language models, yet their formulation assigns each latent feature a single decoder direction, implicitly assuming features to be one-dimensional. We show that this assumption mismatches with the multi-dimensional structure of model features, provably inducing feature splitting through two distinct mechanisms. Geometrically, reconstructing a feature of intrinsic dimension $d_i \ge 2$ to error $\varepsilon$ with single-direction decoders forces a number of atoms that is exponential in $d_i$. From an end-to-end optimization perspective, this splitting is not merely possible but actively preferred. We prove that there exists a continuous path from the true $d_i$-dimensional basis to a strictly lower risk of the $\ell_1$-regularized SAE objective, whose descent directions drive any trained dictionary into that exponential regime. A single coherent feature is therefore fragmented across many near-collinear latents, producing spurious multiplicity and obscuring the intrinsic geometry. Motivated by this, we introduce Subspace-Aware Sparse Autoencoders (SASA), which replace single-vector decoders with learned decoder subspaces, enforce block sparsity via Top-$s$ group gating, and adapt each group's effective rank with a nuclear-norm regularizer. We then show that once the block size satisfies $r \ge d_i$, a single group not only can represent the entire feature slice but is the global minimizer of the SASA objective. This consolidation yields a sample complexity polynomial in $d_i$ rather than exponential -- a decisive advantage given that every training activation costs an LLM forward pass. Empirically, on GPT-2 and Mistral-7B, SASA reduces feature splitting and absorption, improves monosemanticity and interpretability, and matches or exceeds standard SAEs while training on roughly half the token budget.

Asymptotic e-values are emerging as a powerful alternative to asymptotic p-values, particularly in post-hoc inference and multiple testing, where significance levels may be data-dependent. Existing asymptotic e-values, however, suffer from the ``missing factor,'' a scaling inefficiency resulting in overly conservative inference. Drawing on the framework of near-optimal concentration inequalities developed by Bentkus in the 2000s, we introduce Bentkus-type asymptotic e-values and prove that they successfully eliminate the missing factor. We also demonstrate both theoretically and empirically that Bentkus-type e-values consistently deliver sharper inference than existing alternatives, leading to tighter post-hoc confidence intervals and higher rejection rates in multiple testing procedures.

We investigate the heat transport in porous media convection over a wide Rayleigh--Darcy number range of $26.8\leq Ra\leq 2.62\times 10^5$, and a Darcy number range of $6.18\times10^{-7}\leq Da\leq 1.21\times 10^{-5}$. In the experiments, we employ 3D-printed lattice structures as the solid porous matrix and water as the working fluid. Quantitative analyses of the porous medium Nusselt number $Nu_m$ and local temperature statistics reveal that the present system undergoes a transition through five distinct regimes: I. Conduction, II. Convection, III. Oscillation, IV. Transition, V. Classical Rayleigh--Bénard convection. This transitional process bridges the gap between Rayleigh--Darcy-like behaviour and Rayleigh--Bénard-like behaviour in porous media convection. By varying the permeability of the matrix, we further examine the role of the Darcy number $Da$, which turns out to have a profound impact on the transitional processes across different regimes. Flow field measurements reveal that the flow structures within Regime IV and Regime V evolve from several horizontally stacked convection rolls to a single-roll structure, and the pore-scale Reynolds number both exceeds unity in these two regimes. Finally, we report the corresponding phase diagram in the $Ra$-$Da$ space.

We construct data-driven solutions to the Hubble tension, in light of recent data from the Atacama Cosmology Telescope (ACT DR6) and the Dark Energy Spectroscopic Instrument (DESI DR2). We search for the minimal modification to the recombination history through a time-varying electron mass $m_e(z)$ that increases the best-fit $H_0$ inferred from CMB data toward the SH0ES value, without worsening the fit to the data. Using Planck and ACT data including lensing, we find a perturbative modification to $m_e(z)$ that fully resolves the Hubble tension, with the solution sharing the same qualitative oscillatory structure as in previous work using Planck data alone, demonstrating its robustness to the inclusion of more precise and independent CMB data. As a byproduct, the solution also eases the $S_8$ tension. Once DESI DR2 BAO data are added, however, perturbative modifications to $m_e(z)$ cannot fully resolve the Hubble tension. This reflects the same fundamental limitation: raising $H_0$ by modifying recombination generically lowers $Ω_m$, being inconsistent with late-time cosmological observations.