Tootfinder

The Price of Being Partial: Complexity of Partial Generalized Dominating Set on Bounded-Treewidth Graphs
Jakob Greilhuber, D\'aniel Marx
https://arxiv.org/abs/2506.01645

The Price of Being Partial: Complexity of Partial Generalized Dominating Set on Bounded-Treewidth Graphs
The $(σ, ρ)$-domination framework introduced by Telle [Nord. J. Comput.'94] captures many classical graph problems. For fixed sets $σ, ρ$ of non-negative integers, a $(σ,ρ)$-set of a graph $G$ is a set $S$ such that for every $v\in V(G)$, we have (1) if $v \in S$, then $|N(v) \cap S| \in σ$, and (2) if $v \not\in S$, then $|N(v) \cap S| \in ρ$. We initiate the study of a natural partial variant of the problem, in which the constraints given by $σ, ρ$ need not be fu…

Today’s our last full day up north so we’re hanging out on Thunder Bay even though it’s a little cloudy and a little windy and a little chilly.
Lynn’s labyrinth amazingly survives from year to year (with a little annual TLC).

Partially-shared Imaging Regression on Integrating Heterogeneous Brain-Cognition Associations across Alzheimer's Diagnoses
Yang Sui, Qi Xu, Ting Li, Yang Bai, Annie Qu
https://arxiv.org/abs/2505.24259

Partially-shared Imaging Regression on Integrating Heterogeneous Brain-Cognition Associations across Alzheimer's Diagnoses
This paper is motivated by the heterogeneous associations among demographic covariates, imaging data, and cognitive performances across different diagnostic groups within the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We propose a novel PArtially-shared Imaging Regression (PAIR) model with smooth spatial component integration to capture heterogeneous imaging coefficients across multiple data sources. The model assumes that each imaging coefficient can be represented as a weighted…

This https://arxiv.org/abs/2501.16815 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination
This paper introduces two novel criteria: one for feature selection and another for feature elimination in the context of best subset selection, which is a benchmark problem in statistics and machine learning. From the perspective of optimization, we revisit the classical selection and elimination criteria in traditional best subset selection algorithms, revealing that these classical criteria capture only partial variations of the objective function after the entry or exit of features. By form…

Hey! Just a quick snap from today's walk (okay about 4:30h) near #kirchseeon and #ebersberg.
It's been warm but really beautiful. A lot of shade, quite some old trees, lots of green 😊
I'm curious how the photos will turn out. - Now I'm thinking about tomorrow's a…

Diffusion model for gradient preconditioning in hyperspectral imaging inverse problems
Jonathan Monsalve, Kumar Vijay Mishra
https://arxiv.org/abs/2507.23065 https://

Diffusion model for gradient preconditioning in hyperspectral imaging inverse problems
Recovering high-dimensional statistical structure from limited measurements is a fundamental challenge in hyperspectral imaging, where capturing full-resolution data is often infeasible due to sensor, bandwidth, or acquisition constraints. A common workaround is to partition measurements and estimate local statistics-such as the covariance matrix-using only partial observations. However, this strategy introduces noise in the optimization gradients, especially when each partition contains few sa…

Deep Semiparametric Partial Differential Equation Models
Ziyuan Chen, Shunxing Yan, Fang Yao
https://arxiv.org/abs/2506.22779 https://

Deep Semiparametric Partial Differential Equation Models
In many scientific fields, the generation and evolution of data are governed by partial differential equations (PDEs) which are typically informed by established physical laws at the macroscopic level to describe general and predictable dynamics. However, some complex influences may not be fully captured by these laws at the microscopic level due to limited scientific understanding. This work proposes a unified framework to model, estimate, and infer the mechanisms underlying data dynamics. We …

Raining Sessions ☔️
雨 ☔️
📷 Pentax# MX
🎞️Fomapan 400 Action
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

A Ranking Framework for Network Resource Allocation and Scheduling via Hypergraphs
Rajpreet Singh, Novak Bo\v{s}kov, Aditya Gudal, Manzoor A. Khan
https://arxiv.org/abs/2506.01571

A Ranking Framework for Network Resource Allocation and Scheduling via Hypergraphs
Resource allocation and scheduling are a common problem in various distributed systems. Although widely studied, the state-of-the-art solutions either do not scale or lack the expressive power to capture the most complex instances of the problem. To that end, we present a mathematical framework for hypergraph ranking and analysis, unifying graph theory, lattice theory, and semantic analysis. In our fundamental theorem, we prove the existence of partial order on entities of hypergraphs, extendin…

Natura Urbana VI 🏡
城市自然 VI 🏡
📷 Nikon FE
🎞️Ilford FP4 Plus, expired 1995
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

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