Tootfinder

Can all German genocide deniers please get the fuck out of my mentions.
Genocide denial is the last and final stage of genocide and constitutes complicity in genocide.
Matze Schmidt, you are complicit in genocide.
And history is taking notes.
https://masto.ai/@matzeschmidt/1150655

Underground ⤵️
地下 ⤵️
📷 Minolta Hi-Matic AF
🎞️ERA 100, expired 1994
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/1]:
- Automatic Geometric Decomposition for Analytical Inverse Kinematics
Daniel Ostermeier, Jonathan K\"ulz, Matthias Althoff

Equitably Coloring Planar and Outerplanar Graphs
Daniel W. Cranston, Reem Mahmoud
https://arxiv.org/abs/2509.16123 https://arxiv.org/pdf/2509.16123

Equitably Coloring Planar and Outerplanar Graphs
A proper $s$-coloring of an $n$-vertex graph is \emph{equitable} if every color class has size $\lfloor{n/s}\rfloor$ or $\lceil{n/s}\rceil$. A necessary condition to have an equitable $s$-coloring is that every vertex $v$ appears in an independent set of size at least $\lfloor{n/s}\rfloor$. That is $\min_{v\in V(G)}α_v\ge \lfloor{n/s}\rfloor$. Various authors showed that when $G$ is a tree and $s\ge 3$ this obvious necessary condition is also sufficient. Kierstead, Kostochka, and Xiang asked w…

Distributed Functional Optimization and Learning on Banach Spaces: Generic Frameworks
Zhan Yu, Zhongjie Shi, Deming Yuan, Daniel W. C. Ho
https://arxiv.org/abs/2509.17554 https:…

Distributed Functional Optimization and Learning on Banach Spaces: Generic Frameworks
In this paper, we establish a distributed functional optimization (DFO) theory based on time-varying multi-agent networks. The vast majority of existing distributed optimization theories are developed based on Euclidean decision variables. However, for many scenarios in machine learning and statistical learning, such as reproducing kernel spaces or probability measure spaces that use functions or probability measures as fundamental variables, the development of existing distributed optimization…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Evaluating Autoencoders for Parametric and Invertible Multidimensional Projections
Frederik L. Dennig, Nina Geyer, Daniela Blumberg, Yannick Metz, Daniel A. Keim

Well-posedness and Rayleigh-Taylor instability of the two-phase periodic quasistationary Stokes flow
Daniel B\"ohme, Bogdan-Vasile Matioc
https://arxiv.org/abs/2508.15502 h…

Well-posedness and Rayleigh-Taylor instability of the two-phase periodic quasistationary Stokes flow
We study the two-phase, horizontally periodic, quasistationary Stokes flow in two dimensions driven by surface tension and gravity effects in the general context of fluids with (possibly) different viscosities and densities. The sharp interface which separates the fluids is assumed to be the graph of a periodic function. The mathematical model is then recast as a fully nonlinear and nonlocal evolution equation involving only the function parametrizing the interface. Our main results include wel…

Quantum Sampling and Moment Estimation for Transformed Gaussian Random Fields
Matthias Deiml, Daniel Peterseim
https://arxiv.org/abs/2508.13879 https://arx…

Quantum Sampling and Moment Estimation for Transformed Gaussian Random Fields
We present a quantum algorithm for efficiently sampling transformed Gaussian random fields on $d$-dimensional domains, based on an enhanced version of the classical moving average method. Pointwise transformations enforcing boundedness are essential for using Gaussian random fields in quantum computation and arise naturally, for example, in modeling coefficient fields representing microstructures in partial differential equations. Generating this microstructure from its few statistical paramete…

Distributed Stochastic Optimization under Heavy-Tailed Noise: A Federated Mirror Descent Approach with High Probability Convergence
Zhan Yu, Lan Liao, Deming Yuan, Daniel W. C. Ho, Ding-Xuan Zhou
https://arxiv.org/abs/2509.17659

Distributed Stochastic Optimization under Heavy-Tailed Noise: A Federated Mirror Descent Approach with High Probability Convergence
We study the distributed stochastic optimization (DSO) problem under a heavy-tailed noise condition by utilizing a multi-agent system. Despite the extensive research on DSO algorithms used to solve DSO problems under light-tailed noise conditions (such as Gaussian noise), there is a significant lack of study of DSO algorithms in the context of heavy-tailed random noise. Classical DSO approaches in a heavy-tailed setting may present poor convergence behaviors. Therefore, developing DSO methods i…

Measuring IIA Violations in Similarity Choices with Bayesian Models
Hugo Sales Corr\^ea, Suryanarayana Sankagiri, Daniel Ratton Figueiredo, Matthias Grossglauser
https://arxiv.org/abs/2508.14615

Measuring IIA Violations in Similarity Choices with Bayesian Models
Similarity choice data occur when humans make choices among alternatives based on their similarity to a target, e.g., in the context of information retrieval and in embedding learning settings. Classical metric-based models of similarity choice assume independence of irrelevant alternatives (IIA), a property that allows for a simpler formulation. While IIA violations have been detected in many discrete choice settings, the similarity choice setting has received scant attention. This is because …