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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements
Raphael Fischer
https://arxiv.org/abs/2509.22092 https://arxiv.o…

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements
Although machine learning (ML) and artificial intelligence (AI) present fascinating opportunities for innovation, their rapid development is also significantly impacting our environment. In response to growing resource-awareness in the field, quantification tools such as the ML Emissions Calculator and CodeCarbon were developed to estimate the energy consumption and carbon emissions of running AI models. They are easy to incorporate into AI projects, however also make pragmatic assumptions and …

Just noticed that the 30 themes for the 2025 #30DayMapChallenge are now up!
#GISchat

CreAgentive: An Agent Workflow Driven Multi-Category Creative Generation Engine
Yuyang Cheng, Linyue Cai, Changwei Peng, Yumiao Xu, Rongfang Bie, Yong Zhao
https://arxiv.org/abs/2509.26461

CreAgentive: An Agent Workflow Driven Multi-Category Creative Generation Engine
We present CreAgentive, an agent workflow driven multi-category creative generation engine that addresses four key limitations of contemporary large language models in writing stories, drama and other categories of creatives: restricted genre diversity, insufficient output length, weak narrative coherence, and inability to enforce complex structural constructs. At its core, CreAgentive employs a Story Prototype, which is a genre-agnostic, knowledge graph-based narrative representation that deco…

ich werde mir auch so einen schriftzug übers klo häng
https://www.sueddeutsche.de/panorama/weisses-haus-donald-trump-tuerschmuck-gold-reaktion-li.3335208

GastroViT: A Vision Transformer Based Ensemble Learning Approach for Gastrointestinal Disease Classification with Grad CAM & SHAP Visualization
Sumaiya Tabassum, Md. Faysal Ahamed, Hafsa Binte Kibria, Md. Nahiduzzaman, Julfikar Haider, Muhammad E. H. Chowdhury, Mohammad Tariqul Islam
https://arxiv.org/abs/2509.26502

GastroViT: A Vision Transformer Based Ensemble Learning Approach for Gastrointestinal Disease Classification with Grad CAM & SHAP Visualization
The gastrointestinal (GI) tract of humans can have a wide variety of aberrant mucosal abnormality findings, ranging from mild irritations to extremely fatal illnesses. Prompt identification of gastrointestinal disorders greatly contributes to arresting the progression of the illness and improving therapeutic outcomes. This paper presents an ensemble of pre-trained vision transformers (ViTs) for accurately classifying endoscopic images of the GI tract to categorize gastrointestinal problems and …