Tootfinder

We bought into the "post scarcity" narrative because digital tech feels so magic and ethereal but we need to get back to an understanding of the material costs and effects of those systems. They are not very post-scarce in reality.

Just started to read a #VOX article about the glorious AI future but I had to quit right away at this nugget: „we could all be so much more wealthier than we can even imagine today…AI and robots will be able to produce a lot more goods and services than when we have in today’s economy.“
Yeah. Because the lack of stuff that is churned out of factories and the artificial scarcity of digital goods…

A Computational Framework for Interpretable Text-Based Personality Assessment from Social Media
Matej Gjurkovi\'c
https://arxiv.org/abs/2510.02811 https://

A Computational Framework for Interpretable Text-Based Personality Assessment from Social Media
Personality refers to individual differences in behavior, thinking, and feeling. With the growing availability of digital footprints, especially from social media, automated methods for personality assessment have become increasingly important. Natural language processing (NLP) enables the analysis of unstructured text data to identify personality indicators. However, two main challenges remain central to this thesis: the scarcity of large, personality-labeled datasets and the disconnect betwee…

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Aueaphum Aueawatthanaphisut
https://arxiv.org/abs/2510.00976 https://

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Rare-disease diagnosis remains one of the most pressing challenges in digital health, hindered by extreme data scarcity, privacy concerns, and the limited resources of edge devices. This paper proposes the Adaptive Federated Few-Shot Rare-Disease Diagnosis (AFFR) framework, which integrates three pillars: (i) few-shot federated optimization with meta-learning to generalize from limited patient samples, (ii) energy-aware client scheduling to mitigate device dropouts and ensure balanced participa…

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Aueaphum Aueawatthanaphisut
https://arxiv.org/abs/2510.00976 https://

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Rare-disease diagnosis remains one of the most pressing challenges in digital health, hindered by extreme data scarcity, privacy concerns, and the limited resources of edge devices. This paper proposes the Adaptive Federated Few-Shot Rare-Disease Diagnosis (AFFR) framework, which integrates three pillars: (i) few-shot federated optimization with meta-learning to generalize from limited patient samples, (ii) energy-aware client scheduling to mitigate device dropouts and ensure balanced participa…