Tootfinder

Join the 2025 Maine AI 'unconference' on June 13 in Orono for face-to-face discussions whose topics will be democratically chosen by the attenders. (We've got expert panels too.) Register by this Friday—more at https://www.linkedin.com/posts/jonippolito

Modelling shadows in scattered light observations as signals from companions in protoplanetary discs
Deniz Akansoy, Helen C. Petrou, Giulia Ballabio, Anna B. T. Penzlin
https://arxiv.org/abs/2506.04415

Modelling shadows in scattered light observations as signals from companions in protoplanetary discs
Over the past decade, SPHERE scattered light observations of protoplanetary discs have revealed previously unseen features with unprecedented resolution. One such feature are radial streaks of reduced brightness that are commonly interpreted as shadows. A possible cause for these shadows is an embedded companion within the disc. In this work, we use 3D radiative transfer simulations with RADMC-3D to investigate the shadowing effects of embedded companions across a range of orbital distances (5-…

One aspect that's often overlooked with dwm is its portability and uniqueness.
The dwm I'm sporting is one that no one else has; it is totally mine and in that sense unique.You build your own program, patches, binds et al.
Making sure all prerequisites are fulfilled you can then just copy your suckless configs to basically any distro and run a make clean install.
That's why I learned to love dwm and why it's still the best wm around, wayland or not.

Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes
Yu Tan, Yongxiang Li, Xiaowu Dai, Kwok-Leung Tsui
https://arxiv.org/abs/2506.12701

Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes
Functional ANOVA (FANOVA) is a widely used variance-based sensitivity analysis tool. However, studies on functional-output FANOVA remain relatively scarce, especially for black-box computer experiments, which often involve complex and nonlinear functional-output relationships with unknown data distribution. Conventional approaches often rely on predefined basis functions or parametric structures that lack the flexibility to capture complex nonlinear relationships. Additionally, strong assumptio…

Sensor Model Identification via Simultaneous Model Selection and State Variable Determination
Christian Brommer, Alessandro Fornasier, Jan Steinbrener, Stephan Weiss
https://arxiv.org/abs/2506.11263

Sensor Model Identification via Simultaneous Model Selection and State Variable Determination
We present a method for the unattended gray-box identification of sensor models commonly used by localization algorithms in the field of robotics. The objective is to determine the most likely sensor model for a time series of unknown measurement data, given an extendable catalog of predefined sensor models. Sensor model definitions may require states for rigid-body calibrations and dedicated reference frames to replicate a measurement based on the robot's localization state. A health metric is…