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The Pontryagin-Thom theorem for families of framed manifolds arxiv.org/abs/2503.20818

Uniform validity of atomic Kreisel-Putnam rule in monotonic proof-theoretic semantics arxiv.org/abs/2503.19930

Uniform validity of atomic Kreisel-Putnam rule in monotonic proof-theoretic semantics

Proof-theoretic semantics (PTS) is normally understood today as Base-Extension Semantics (B-eS), i.e., as a theory of proof-theoretic consequence over atomic proof systems. Intuitionistic logic (IL) has been proved to be incomplete over a number of variants of B-eS, including a monotonic one where introduction rules play a prior role (miB-eS). In its original formulation by Prawitz, however, PTS consequence is not a primitive, but a derived notion. The main concept is that of argument structure valid relative to atomic systems and assignments of reductions for eliminating generalised detours of inferences in non-introduction form. This is called Proof-Theoretic Validity (P-tV), and it can be given in a monotonic and introduction-based form too (miP-tV). It is unclear whether, and under what conditions, the incompleteness results proved for IL over miB-eS can be transferred to miP-tV. As has been remarked, the main problem seems to be that the notion of argumental validity underlying the miB-eS notion of consequence is one where reductions are either forced to be non-uniform, or non-constructive. Building on some Prawitz-fashion incompleteness proofs for IL based on the notion of (intuitionistic) construction, I provide in what follows a set of reductions which are surely uniform (however uniformity is defined) and constructive, and which make atomic Kreisel-Putnam rule logically valid over miP-tV, thus implying the incompleteness of IL over a Prawitzian (monotonic, introduction-based) framework strictly understood.

arXiv.org

Global Well-Posedness for the 3D Navier-Stokes Equations under Logarithmically Improved Criteria: Connections to Turbulence Theory arxiv.org/abs/2503.19944

Global Well-Posedness for the 3D Navier-Stokes Equations under Logarithmically Improved Criteria: Connections to Turbulence Theory

This paper introduces a novel class of initial data for which the three-dimensional incompressible Navier--Stokes equations yield unique global-in-time solutions. Building on a logarithmically improved regularity criterion, we impose a logarithmically subcritical condition on the initial data. Specifically, if \[ u_0 \in L^2(\mathbb{R}^3) \quad \text{and} \quad \|(-Δ)^{s/2}u_0\|_{L^q(\mathbb{R}^3)} \le \frac{C_0}{\Bigl(1+\log\bigl(e+\|u_0\|_{\dot{H}^s}\bigr)\Bigr)^δ}, \] for some $s \in (1/2,1)$ under appropriate scaling, then the corresponding solution exists globally and is unique. The proof employs refined commutator estimates for the fractional Laplacian together with new energy methods that exploit this logarithmic improvement to prevent singularity formation. Furthermore, we establish links between these improved criteria and turbulence theory. We derive precise relationships connecting the regularity conditions with turbulent intermittency, showing that the logarithmic enhancements correspond to anomalous scaling exponents in the turbulent energy spectrum. Additionally, we characterize the local structure of potential singularities and provide tight bounds on the energy flux in turbulent cascades. This approach bridges the gap between subcritical and critical regularity for the Navier--Stokes equations and offers a robust mathematical foundation for key phenomena observed in turbulence.

arXiv.org

Bridging the Gap Between Contextual and Standard Stochastic Bilevel Optimization arxiv.org/abs/2503.19991

Bridging the Gap Between Contextual and Standard Stochastic Bilevel Optimization

Contextual Stochastic Bilevel Optimization (CSBO) extends standard Stochastic Bilevel Optimization (SBO) by incorporating context-specific lower-level problems, which arise in applications such as meta-learning and hyperparameter optimization. This structure imposes an infinite number of constraints - one for each context realization - making CSBO significantly more challenging than SBO as the unclear relationship between minimizers across different contexts suggests computing numerous lower-level solutions for each upper-level iteration. Existing approaches to CSBO face two major limitations: substantial complexity gaps compared to SBO and reliance on impractical conditional sampling oracles. We propose a novel reduction framework that decouples the dependence of the lower-level solution on the upper-level decision and context through parametrization, thereby transforming CSBO into an equivalent SBO problem and eliminating the need for conditional sampling. Under reasonable assumptions on the context distribution and the regularity of the lower-level, we show that an $ε$-stationary solution to CSBO can be achieved with a near-optimal sampling complexity $\tilde{O}(ε^{-3})$. Our approach enhances the practicality of solving CSBO problems by improving both computational efficiency and theoretical guarantees.

arXiv.org
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