Tootfinder

Boston Globe editor Nancy Barnes steps down from her role but will remain on staff as editor-at-large; she joined the paper as the first female editor in 2023 (Aidan Ryan/The Boston Globe)
https://www.bostonglobe.com/2025/12/12/business/nanc…

Boston Globe editor Nancy Barnes to step down
In a note to the Globe newsroom, Barnes said she is stepping down at the end of next week and will stay on staff as an editor-at-large.

Camera-trap study catches nectar-feeding birds breaking into flowers when their beaks don't fit
Trait matching affects the probability of nectar robbing in plant-pollinator networks
https://doi.org/10.1002/oik.11552

Lego table is coming along. The center piece is done. And ready to be finished. I need to make the legs for both sides (4x) and the two leaves.
We have a good idea on the finish. The "walnut" color on the top right is so far the preference.
#woodworking
#inprogress

Dänemark schafft die Post ab! Wir sollten dem Beispiel folgen und zum Schutze der Kinder Papier verbieten!
oder: Wie Diskussionen um Digitales und Kinder & Jugendliche oft ablaufen...
https://blog.doebe.li/Blog/DaenemarkSchafftDiePostAb

Foundation Models for Astrobiology: Paper I -- Workshop and Overview
Ryan Felton, Caleb Scharf, Stuart Bartlett, Nathalie A. Cabrol, Victoria Da Poian, Diana Gentry, Jian Gong, Adrienne Hoarfrost, Manil Maskey, Floyd Nichols, Conor A. Nixon, Tejas Panambur, Joseph Pasterski, Anton S. Petrov, Anirudh Prabhu, Brenda Thomson, Hamed Valizadegan, Kimberley Warren-Rhodes, David Wettergreen, Michael L. Wong, Anastasia Yanchilina

Foundation Models for Astrobiology: Paper I -- Workshop and Overview
Advances in machine learning over the past decade have resulted in a proliferation of algorithmic applications for encoding, characterizing, and acting on complex data that may contain many high dimensional features. Recently, the emergence of deep-learning models trained across very large datasets has created a new paradigm for machine learning in the form of Foundation Models. Foundation Models are programs trained on very large and broad datasets with an extensive number of parameters. Once …

McMining: Automated Discovery of Misconceptions in Student Code
Erfan Al-Hossami, Razvan Bunescu
https://arxiv.org/abs/2510.08827 https://arxiv.org/pdf/251…

McMining: Automated Discovery of Misconceptions in Student Code
When learning to code, students often develop misconceptions about various programming language concepts. These can not only lead to bugs or inefficient code, but also slow down the learning of related concepts. In this paper, we introduce McMining, the task of mining programming misconceptions from samples of code from a student. To enable the training and evaluation of McMining systems, we develop an extensible benchmark dataset of misconceptions together with a large set of code samples wher…

Why Trump is Doomed to Lose the Affordability Fight (Dan Pfeiffer/The Message Box)
https://www.messageboxnews.com/p/why-trump-is-doomed-to-lose-the-affordability
http://www.memeorandum.com/251211/p31#a251211p31

O_O-VC: Synthetic Data-Driven One-to-One Alignment for Any-to-Any Voice Conversion
Huu Tuong Tu, Huan Vu, cuong tien nguyen, Dien Hy Ngo, Nguyen Thi Thu Trang
https://arxiv.org/abs/2510.09061

O_O-VC: Synthetic Data-Driven One-to-One Alignment for Any-to-Any Voice Conversion
Traditional voice conversion (VC) methods typically attempt to separate speaker identity and linguistic information into distinct representations, which are then combined to reconstruct the audio. However, effectively disentangling these factors remains challenging, often leading to information loss during training. In this paper, we propose a new approach that leverages synthetic speech data generated by a high-quality, pretrained multispeaker text-to-speech (TTS) model. Specifically, syntheti…

Sequencing on Silicon: AI SoC Design for Mobile Genomics at the Edge
Sebastian Magierowski, Zhongpan Wu, Abel Beyene, Karim Hammad
https://arxiv.org/abs/2510.09339 https://

Sequencing on Silicon: AI SoC Design for Mobile Genomics at the Edge
Miniature DNA sequencing hardware has begun to succeed in mobile contexts, driving demand for efficient machine learning at the edge. This domain leverages deep learning techniques familiar from speech and time-series analysis for both low-level signal processing and high-level genomic interpretation. Unlike audio, however, nanopore sequencing presents raw data rates over 100X higher, requiring more aggressive compute and memory handling. In this paper, we present a CMOS system-on-chip (SoC) de…