Tootfinder

Interface on demand: Towards AI native Control interfaces for 6G
Abhishek Dandekar, Prashiddha D. Thapa, Ashrafur Rahman, Julius Schulz-Zander
https://arxiv.org/abs/2508.15595 h…

Interface on demand: Towards AI native Control interfaces for 6G
Traditional standardized network interfaces face significant limitations, including vendor-specific incompatibilities, rigid design assumptions, and lack of adaptability for new functionalities. We propose a multi-agent framework leveraging large language models (LLMs) to generate control interfaces on demand between network functions (NFs). This includes a matching agent, which aligns required control functionalities with NF capabilities, and a code-generation agent, which generates the necess…

Forward and inverse problems for a mixed-type equation with the Caputo fractional derivative and Dezin-type non-local condition
Ravshan Ashurov, Umida Dusanova, Navbahor Nuraliyeva
https://arxiv.org/abs/2507.12129

Forward and inverse problems for a mixed-type equation with the Caputo fractional derivative and Dezin-type non-local condition
This work is dedicated to the study of a mixed-type partial differential equation involving a Caputo fractional derivative in the time domain $t > 0$ and a classical parabolic equation in the domain $t < 0$, along with Dezin-type non-local boundary and gluing conditions. The forward and inverse problems are studied in detail. For the forward problem, the existence and uniqueness of solutions are established using the Fourier method, under appropriate assumptions on the initial data and the righ…

A Classification-Driven Likelihood Ratio Method for Familial DNA Testing
Akaraphon Jantaraphum, Chanagarn Laoiam, Budsaba Rerkamnuaychoke, Jittima Shotivaranon, Monchai Kooakachai
https://arxiv.org/abs/2508.15579

A Classification-Driven Likelihood Ratio Method for Familial DNA Testing
Background: Familial DNA search relies on statistical methods to infer genetic relationships, often using the likelihood ratio (LR) as the test statistic. This approach involves calculating the probability of observed DNA profiles under various scenarios, traditionally assuming a uniform genetic background for all individuals. However, this assumption is unrealistic in practice due to population substructure, where allele frequencies differ among subpopulations. To address this, modified LR-bas…