Deep Randomized Distributed Function Computation (DeepRDFC): Neural Distributed Channel Simulation

This paper proposes a deep learning-based autoencoder architecture for the Randomized Distributed Function Computation (RDFC) framework that minimizes the total variation distance to an unknown target distribution using only data samples, demonstrating superior communication efficiency compared to traditional data compression methods, particularly under limited common randomness.

Didrik Bergström, Onur Günlü2026-03-12🔢 math

Extremal problems in uniformly dense hypergraphs and digraphs

This paper establishes a novel connection between digraph extremal problems and uniform Turán densities of 3-graphs to provide verifiable conditions for determining specific density values, including identifying new classes of 3-graphs with densities such as (r1)/r(r-1)/r, (r1)2/r2(r-1)^2/r^2, and $4/27,whilealsoofferingasimplifiedprooffortheexistenceof3graphswithdensity, while also offering a simplified proof for the existence of 3-graphs with density 1/27$.

Hao Lin, Guanghui Wang, Wenling Zhou, Yiming Zhou2026-03-12🔢 math

On the ubiquity of uniformly dominant local rings

This paper establishes that a Cohen-Macaulay complete local ring with an infinite residue field is uniformly dominant with explicit bounds on its dominant index under various conditions, including codimension 2 non-complete intersections, Burch rings, quasi-fiber product rings, and rings with low multiplicity, thereby recovering and refining existing results on hypersurfaces and specific ring classes.

Toshinori Kobayashi, Ryo Takahashi2026-03-12🔢 math

Spectral deviation of concentration operators on reproducing kernel Hilbert spaces

This paper establishes a unified framework for estimating the spectral deviation of concentration operators on reproducing kernel Hilbert spaces, demonstrating that discretized approximations like Gabor multipliers preserve the theoretical localization properties of their continuous counterparts with bounds uniform in the discretization step.

Felipe Marceca, José Luis Romero, Michael Speckbacher, Lisa Valentini2026-03-12🔢 math

Offset Pointing for Energy-efficient Reception in Underwater Optical Wireless Communication: Modeling and Performance Analysi

This paper proposes a stochastic geometry framework for Underwater Optical Wireless Communication that reveals a counter-intuitive "offset pointing" strategy, where intentionally misaligning the receiver by an optimal angle maximizes received power and reduces transmit power requirements by nearly 20%, thereby significantly extending network lifetime and improving energy efficiency.

Qiyu Ma, Jiajie Xu, Mohamed-Slim Alouini2026-03-12🔢 math