Tootfinder

Ukraine meldet russische Angriffe
Russland hat die Ukraine in der Nacht zu Dienstag mit Kampfdrohnen angegriffen. Der Luftalarm dauerte in einigen Regionen bis in die Morgenstunden, wie die ukrainische Luftwaffe mitteilte. Im nordöstlichen Gebiet Sumy wurden nach Behördenangaben drei Menschen leicht verletzt. 47 von 59 Drohnen seien abgefangen worden.
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Ukraine-Liveblog: ++ Selenskyj macht Druck bei Sicherheitsgarantien ++
Der ukrainische Präsident Selenskyj fordert eine schnellere Ausarbeitung möglicher Sicherheitsgarantien für sein Land. Eine fehlgeleitete Drohne der Ukraine ist nach Behördenangaben in Estland abgestürzt.

Predict, Reposition, and Allocate: A Greedy and Flow-Based Architecture for Sustainable Urban Food Delivery
Aqsa Ashraf Makhdomi, Iqra Altaf Gillani
https://arxiv.org/abs/2507.15282

Predict, Reposition, and Allocate: A Greedy and Flow-Based Architecture for Sustainable Urban Food Delivery
The rapid proliferation of food delivery platforms has reshaped urban mobility but has also contributed significantly to environmental degradation through increased greenhouse gas emissions. Existing optimization mechanisms produce sub-optimal outcomes as they do not consider environmental sustainability their optimization objective. This study proposes a novel eco-friendly food delivery optimization framework that integrates demand prediction, delivery person routing, and order allocation to m…

Inductive Bias Extraction and Matching for LLM Prompts
Christian M. Angel, Francis Ferraro
https://arxiv.org/abs/2508.10295 https://arxiv.org/pdf/2508.1029…

Inductive Bias Extraction and Matching for LLM Prompts
The active research topic of prompt engineering makes it evident that LLMs are sensitive to small changes in prompt wording. A portion of this can be ascribed to the inductive bias that is present in the LLM. By using an LLM's output as a portion of its prompt, we can more easily create satisfactory wording for prompts. This has the effect of creating a prompt that matches the inductive bias in model. Empirically, we show that using this Inductive Bias Extraction and Matching strategy improves …

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming
Humza Ashraf, Syed Muhammad Danish, Aris Leivadeas, Yazan Otoum, Zeeshan Sattar
https://arxiv.org/abs/2508.08332

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming
Large Language Models (LLMs) are widely used for code generation. However, commercial models like ChatGPT require significant computing power, which leads to high energy use and carbon emissions. This has raised concerns about their environmental impact. In this study, we evaluate open-source Small Language Models (SLMs) trained explicitly for code generation and compare their performance and energy efficiency against large LLMs and efficient human-written Python code. The goal is to investigat…

🇺🇦 #NowPlaying on BBCRadio3's #ThroughTheNight
Adrien-François Servais, Trad. & Adrien-Francois Servais Ensemble:
🎵 La Romanesca
#AdrienFrançoisServais #Trad #AdrienFrancoisServaisEnsemble