Tootfinder

A look at the rise of $5B unicorns, which make up 13% of ~1,600 unicorns but hold $3.5T of $6T total valuation; 17 startups joined the $5B club in H1 2025 (Gené Teare/Crunchbase News)
https://news.crunchbase.com/venture/ultra-unicorn-startups…

The Rise Of The Ultra-Unicorns: $5B+ Startups Are Leading The Private-Market Herd In 2025
Of the nearly 1,600 companies on The Crunchbase Unicorn Board, a small but ultra-elite club of private companies valued at $5 billion or more is growing across multiple metrics, an analysis of the data shows. We take a look at them, their valuations and which sectors — and countries — rank the highest.

Krieg in der Ukraine: Was, wenn der Ukraine die Kraft ausgeht?
Viele halten eine Niederlage der Ukraine für unausweichlich. Es wäre eine gewaltige Katastrophe. Unsere Wut und unsere Hilfsbereitschaft müssten viel größer sein meint Wolfgang Schwarz in ZEIT ONLINE
Lest das!
https://www.

Krieg in der Ukraine: Was, wenn der Ukraine die Kraft ausgeht?
Viele halten eine Niederlage der Ukraine für unausweichlich. Es wäre eine gewaltige Katastrophe. Unsere Wut und unsere Hilfsbereitschaft müssten viel größer sein.

So war das … #unicornsfootball #ErimaGFL #AmericanFootball

Ein Ratgeber für gutes Gelingen und unsere Menschlichkeit https://weddingweiser.de/ein-ratgeber-fuer-gutes-gelingen-und-unsere-menschlichkeit/

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields
Sadra Safadoust, Fabio Tosi, Fatma G\"uney, Matteo Poggi
https://arxiv.org/abs/2506.22433

WarpRF: Multi-View Consistency for Training-Free Uncertainty Quantification and Applications in Radiance Fields
We introduce WarpRF, a training-free general-purpose framework for quantifying the uncertainty of radiance fields. Built upon the assumption that photometric and geometric consistency should hold among images rendered by an accurate model, WarpRF quantifies its underlying uncertainty from an unseen point of view by leveraging backward warping across viewpoints, projecting reliable renderings to the unseen viewpoint and measuring the consistency with images rendered there. WarpRF is simple and i…

Uncertainty-aware Planning with Inaccurate Models for Robotized Liquid Handling
Marco Faroni, Carlo Odesco, Andrea Zanchettin, Paolo Rocco
https://arxiv.org/abs/2507.20861 https…

Uncertainty-aware Planning with Inaccurate Models for Robotized Liquid Handling
Physics-based simulations and learning-based models are vital for complex robotics tasks like deformable object manipulation and liquid handling. However, these models often struggle with accuracy due to epistemic uncertainty or the sim-to-real gap. For instance, accurately pouring liquid from one container to another poses challenges, particularly when models are trained on limited demonstrations and may perform poorly in novel situations. This paper proposes an uncertainty-aware Monte Carlo T…

"I never imagined that I would become unchurched. Yet being unchurched, I am discovering that whenever we intentionally put a few chairs and a table together to have a meal, we are manifesting a new—or perhaps ancient—way of doing church in our time."
—Vincent Wei-Cheng Lin ’24 S.T.M. writing in the new issue of Reflections, which addresses whether Christianity is losing its religion.

The programme is live in about 20 minutes over at UncertainFM
https://uncertain.fm/

Digitalizing Uncertain Information
Chris Partridge, Andrew Mitchell, Andreas Cola
https://arxiv.org/abs/2507.21173 https://arxiv.org/pdf/2507.21173

Digitalizing Uncertain Information
The paper sketches some initial results from an ongoing project to develop an ontology-based digital form for representing uncertain information. We frame this work as a journey from lower to higher levels of digital maturity across a technology divide. The paper first sets a baseline by describing the basic challenges any project dealing with digital uncertainty faces. It then describes how the project is facing them. It shows firstly how an extensional ontology (such as the BORO Foundational …

Decentralized Uncertainty-Aware Multi-Agent Collision Avoidance With Model Predictive Path Integral
Stepan Dergachev, Konstantin Yakovlev
https://arxiv.org/abs/2507.20293 https:…

Decentralized Uncertainty-Aware Multi-Agent Collision Avoidance With Model Predictive Path Integral
Decentralized multi-agent navigation under uncertainty is a complex task that arises in numerous robotic applications. It requires collision avoidance strategies that account for both kinematic constraints, sensing and action execution noise. In this paper, we propose a novel approach that integrates the Model Predictive Path Integral (MPPI) with a probabilistic adaptation of Optimal Reciprocal Collision Avoidance. Our method ensures safe and efficient multi-agent navigation by incorporating pr…