Tootfinder

Umfragen zeigen: Kleine Symbole für Gen-Z besonders wichtig
Wer mit der Gen Z arbeitet, sollte allmählich die Sprache der Emojis erlernen. Das legen Umfrageergebnisse nahe.
https://www.

Ungewöhnlich warme #Meeresströmungen vor Japans Küste können zu extremem #Schneefall führen.
Zu diesem Ergebnis kamen Wissenschaftler der Rissho-Universität und der Universität Hokkaido. Im Februar fielen auf

Steigende Meerestemperaturen erhöhen das Risiko für extremen Schneefall in Japan
Ein Forschungsteam aus Japan hat untersucht, warum es im Februar in der Stadt Obihiro auf Hokkaido zu einem außergewöhnlich starken Schneefall kam. Die

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Sampology:
🎵 Morning Sun
#Sampology
https://sampology.bandcamp.com/track/morning-sun
https://open.spotify.com/track/0ODz72aWsdWhKqgEmJYZ7Q

"DHCPv6 PD support is expected to roll out to most devices running Android 11 and above before the end of the year [...]"
https://android-developers.googleblog.com/2025/09/simplifying-advanced-networking-with.html
For bac…

Simplifying advanced networking with DHCPv6 Prefix Delegation
News and insights on the Android platform, developer tools, and events.

Simplifications are Absolutists: How Simplified Language Reduces Word Sense Awareness in LLM-Generated Definitions
Lukas Ellinger, Miriam Ansch\"utz, Georg Groh
https://arxiv.org/abs/2507.11981

Simplifications are Absolutists: How Simplified Language Reduces Word Sense Awareness in LLM-Generated Definitions
Large Language Models (LLMs) can provide accurate word definitions and explanations for any context. However, the scope of the definition changes for different target groups, like children or language learners. This is especially relevant for homonyms, words with multiple meanings, where oversimplification might risk information loss by omitting key senses, potentially misleading users who trust LLM outputs. We investigate how simplification impacts homonym definition quality across three targe…

Robust Multi-Manifold Clustering via Simplex Paths
Haoyu Chen, Anna Little, Akin Narayan
https://arxiv.org/abs/2507.10710 https://arx…

Robust Multi-Manifold Clustering via Simplex Paths
This article introduces a novel, geometric approach for multi-manifold clustering (MMC), i.e. for clustering a collection of potentially intersecting, d-dimensional manifolds into the individual manifold components. We first compute a locality graph on d-simplices, using the dihedral angle in between adjacent simplices as the graph weights, and then compute infinity path distances in this simplex graph. This procedure gives a metric on simplices which we refer to as the largest angle path dista…

Enhancing Symbolic Machine Learning by Subsymbolic Representations
Stephen Roth, Lennart Baur, Derian Boer, Stefan Kramer
https://arxiv.org/abs/2506.14569 …

Enhancing Symbolic Machine Learning by Subsymbolic Representations
The goal of neuro-symbolic AI is to integrate symbolic and subsymbolic AI approaches, to overcome the limitations of either. Prominent systems include Logic Tensor Networks (LTN) or DeepProbLog, which offer neural predicates and end-to-end learning. The versatility of systems like LTNs and DeepProbLog, however, makes them less efficient in simpler settings, for instance, for discriminative machine learning, in particular in domains with many constants. Therefore, we follow a different approach:…

Improving Reinforcement Learning Sample-Efficiency using Local Approximation
Mohit Prashant, Arvind Easwaran
https://arxiv.org/abs/2507.12383 https://

Improving Reinforcement Learning Sample-Efficiency using Local Approximation
In this study, we derive Probably Approximately Correct (PAC) bounds on the asymptotic sample-complexity for RL within the infinite-horizon Markov Decision Process (MDP) setting that are sharper than those in existing literature. The premise of our study is twofold: firstly, the further two states are from each other, transition-wise, the less relevant the value of the first state is when learning the $ε$-optimal value of the second; secondly, the amount of 'effort', sample-complexity-wise, ex…

A Traditional Approach to Symbolic Piano Continuation
Christian Zhou-Zheng, John Backsund, Dun Li Chan, Alex Coventry, Avid Eslami, Jyotin Goel, Xingwen Han, Danysh Soomro, Galen Wei
https://arxiv.org/abs/2509.12267

A Traditional Approach to Symbolic Piano Continuation
We present a traditional approach to symbolic piano music continuation for the MIREX 2025 Symbolic Music Generation challenge. While computational music generation has recently focused on developing large foundation models with sophisticated architectural modifications, we argue that simpler approaches remain more effective for constrained, single-instrument tasks. We thus return to a simple, unaugmented next-token-prediction objective on tokenized raw MIDI, aiming to outperform large foundatio…

An Exact and Efficient Sampler for Dynamic Discrete Distributions
Lilith Orion Hafner, Adriano Meligrana
https://arxiv.org/abs/2506.14062 https://

An Exact and Efficient Sampler for Dynamic Discrete Distributions
Sampling from a dynamic discrete distribution involves sampling from a dynamic set of weighted elements, where elements can be added or removed at any stage of the sampling process. Although efficient for static sets, the Alias method becomes impractical in dynamic settings due to the need to reconstruct the sampler after each update, which incurs a computational cost proportional to the size of the distribution, making it unsuitable for applications requiring frequent insertions, deletions, or…