Tootfinder

Mmmh-Macs: Als Apple Silicon den Macintosh rettete | Mac & i-Podcast
Nervende Lüfter, unzuverlässige Tastaturen und Adapter-Bonanza: MacBooks steckten in der Krise – bis Apple Silicon kam und alles änderte.

"Instead of calculators, we could sit at exams with certain books on our desks, filled with logarithm and trigonometry tables, featuring formulae for algebra, calculus, and geometry. French-speaking Swiss students might remember the ubiquitous “Formulaire et Tables” of the “Commissions romandes de mathématiques, de physique et de chimie”, published by the Éditions du Tricorne, that many generations of Swiss students used during their exams."

Banning, Adopting, Reckoning
Last month, OpenAI, the company (in)famous for their ChatGPT product, released a course called "ChatGPT Foundations for K-12 Educators", an event that has raised more than a few eyebrows, and even some outrage. We must have a serious conversation about the value of a bullshit generator in the context of teaching programming skills to new generations.

Design and Evaluation of a Scalable Data Pipeline for AI-Driven Air Quality Monitoring in Low-Resource Settings
Richard Sserujongi, Daniel Ogenrwot, Nicholas Niwamanya, Noah Nsimbe, Martin Bbaale, Benjamin Ssempala, Noble Mutabazi, Raja Fidel Wabinyai, Deo Okure, Engineer Bainomugisha
https://arxiv.org/abs/2508.14451

Design and Evaluation of a Scalable Data Pipeline for AI-Driven Air Quality Monitoring in Low-Resource Settings
The increasing adoption of low-cost environmental sensors and AI-enabled applications has accelerated the demand for scalable and resilient data infrastructures, particularly in data-scarce and resource-constrained regions. This paper presents the design, implementation, and evaluation of the AirQo data pipeline: a modular, cloud-native Extract-Transform-Load (ETL) system engineered to support both real-time and batch processing of heterogeneous air quality data across urban deployments in Afri…

EMORe: Motion-Robust 5D MRI Reconstruction via Expectation-Maximization-Guided Binning Correction and Outlier Rejection
Syed M. Arshad, Lee C. Potter, Yingmin Liu, Christopher Crabtree, Matthew S. Tong, Rizwan Ahmad
https://arxiv.org/abs/2507.23224

EMORe: Motion-Robust 5D MRI Reconstruction via Expectation-Maximization-Guided Binning Correction and Outlier Rejection
We propose EMORe, an adaptive reconstruction method designed to enhance motion robustness in free-running, free-breathing self-gated 5D cardiac magnetic resonance imaging (MRI). Traditional self-gating-based motion binning for 5D MRI often results in residual motion artifacts due to inaccuracies in cardiac and respiratory signal extraction and sporadic bulk motion, compromising clinical utility. EMORe addresses these issues by integrating adaptive inter-bin correction and explicit outlier rejec…

Wusstest du, dass man aus nur drei Zutaten eine pflegende #OlivenölHonigSalbe herstellen kann? 🧴✨ Es gibt viele DIY-Rezepte für #Kosmetik. Selbermachen kommt zwar an professionelle Produkte nicht immer heran, aber man weiß, was drin ist. Und für manche sind weniger Chemie und keine …

Olivenöl-Honig-Salbe aus nur drei Zutaten selber herstellen
DIY-Salbe gegen rissige Hände: Einfaches Rezept mit Bienenwachs, Olivenöl und Honig

Agentic Semantic Control for Autonomous Wireless Space Networks: Extending Space-O-RAN with MCP-Driven Distributed Intelligence
Eduardo Baena, Paolo Testolina, Michele Polese, Sergi Aliaga, Andrew Benincasa, Dimitrios Koutsonikolas, Josep Jornet, Tommaso Melodia
https://arxiv.org/abs/2506.10925

Agentic Semantic Control for Autonomous Wireless Space Networks: Extending Space-O-RAN with MCP-Driven Distributed Intelligence
Lunar surface operations impose stringent requirements on wireless communication systems, including autonomy, robustness to disruption, and the ability to adapt to environmental and mission-driven context. While Space-O-RAN provides a distributed orchestration model aligned with 3GPP standards, its decision logic is limited to static policies and lacks semantic integration. We propose a novel extension incorporating a semantic agentic layer enabled by the Model Context Protocol (MCP) and Agent-…

From Release to Adoption: Challenges in Reusing Pre-trained AI Models for Downstream Developers
Peerachai Banyongrakkul, Mansooreh Zahedi, Patanamon Thongtanunam, Christoph Treude, Haoyu Gao
https://arxiv.org/abs/2506.23234

From Release to Adoption: Challenges in Reusing Pre-trained AI Models for Downstream Developers
Pre-trained models (PTMs) have gained widespread popularity and achieved remarkable success across various fields, driven by their groundbreaking performance and easy accessibility through hosting providers. However, the challenges faced by downstream developers in reusing PTMs in software systems are less explored. To bridge this knowledge gap, we qualitatively created and analyzed a dataset of 840 PTM-related issue reports from 31 OSS GitHub projects. We systematically developed a comprehensi…

Optimized binning for response function reconstruction via Chebyshev expansions
Immo C. Reis, Joanna E. Sobczyk, Sonia Bacca
https://arxiv.org/abs/2507.00587

Optimized binning for response function reconstruction via Chebyshev expansions
We propose an optimized histogram binning strategy to reconstruct nuclear response functions via the Chebyshev expansion bound-state method. Our approach employs a stochastic regularization of the density of states to define adaptive, equal-area bins. Using the deuteron solved in a harmonic-oscillator basis with a chiral interaction, we benchmark on dipole and longitudinal responses, obtaining excellent agreement with exact theory and experiment. This general framework readily extends to other …