850 research outputs found
Correlation of multiplicative functions over function fields
In this article we study the asymptotic behaviour of the correlation
functions over polynomial ring . Let and
be the set of all monic polynomials and monic irreducible
polynomials of degree over respectively. For multiplicative
functions and on , we obtain asymptotic
formula for the following correlation functions for a fixed and \begin{align*} &S_{2}(n, q):=\displaystyle\sum_{f\in \mathcal{M}_{n,
q}}\psi_1(f+h_1) \psi_2(f+h_2), \\ &R_2(n, q):=\displaystyle\sum_{P\in
\mathcal{P}_{n, q}}\psi_1(P+h_1)\psi_2(P+h_2), \end{align*} where
are fixed polynomials of degree over . As a consequence, for
real valued additive functions and on
we show that for a fixed and , the following
distribution functions \begin{align*} &\frac{1}{|\mathcal{M}_{n,
q}|}\Big|\{f\in \mathcal{M}_{n, q} :
\tilde{\psi_1}(f+h_1)+\tilde{\psi_2}(f+h_2)\leq x\}\Big|,\\ &
\frac{1}{|\mathcal{P}_{n, q}|}\Big|\{P\in \mathcal{P}_{n, q} :
\tilde{\psi_1}(P+h_1)+\tilde{\psi_2}(P+h_2)\leq x\}\Big| \end{align*} converges
weakly towards a limit distribution.Comment: 24 pages; Comments are welcom
M3D-NCA: Robust 3D Segmentation with Built-in Quality Control
Medical image segmentation relies heavily on large-scale deep learning
models, such as UNet-based architectures. However, the real-world utility of
such models is limited by their high computational requirements, which makes
them impractical for resource-constrained environments such as primary care
facilities and conflict zones. Furthermore, shifts in the imaging domain can
render these models ineffective and even compromise patient safety if such
errors go undetected. To address these challenges, we propose M3D-NCA, a novel
methodology that leverages Neural Cellular Automata (NCA) segmentation for 3D
medical images using n-level patchification. Moreover, we exploit the variance
in M3D-NCA to develop a novel quality metric which can automatically detect
errors in the segmentation process of NCAs. M3D-NCA outperforms the two
magnitudes larger UNet models in hippocampus and prostate segmentation by 2%
Dice and can be run on a Raspberry Pi 4 Model B (2GB RAM). This highlights the
potential of M3D-NCA as an effective and efficient alternative for medical
image segmentation in resource-constrained environments
- β¦