Tootfinder

#running slow is hard but my fitness is such that it requires a rebuild of the foundation. Trying to stay in HR2 means a decent amount of walking interspersed. I don’t love it, but it’s necessary.
I have a much more open schedule now, so I should be able to get out and run regularly. The trick will be staying patient and trusting the process.

SingLEM: Single-Channel Large EEG Model
Jamiyan Sukhbaatar, Satoshi Imamura, Ibuki Inoue, Shoya Murakami, Kazi Mahmudul Hassan, Seungwoo Han, Ingon Chanpornpakdi, Toshihisa Tanaka
https://arxiv.org/abs/2509.17920

SingLEM: Single-Channel Large EEG Model
Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability. Although emerging foundation models aim for broader applicability, their rigid dependence on fixed, high-density multi-channel montages restricts their use across heterogeneous datasets and in missing-channel or practical low-channel settings. To address these limitations, we introduce SingLEM, a self-supervised foundation model that learns rob…

CrafterDojo: A Suite of Foundation Models for Building Open-Ended Embodied Agents in Crafter
Junyeong Park, Hyeonseo Cho, Sungjin Ahn
https://arxiv.org/abs/2508.13530 https://…

CrafterDojo: A Suite of Foundation Models for Building Open-Ended Embodied Agents in Crafter
Developing general-purpose embodied agents is a core challenge in AI. Minecraft provides rich complexity and internet-scale data, but its slow speed and engineering overhead make it unsuitable for rapid prototyping. Crafter offers a lightweight alternative that retains key challenges from Minecraft, yet its use has remained limited to narrow tasks due to the absence of foundation models that have driven progress in the Minecraft setting. In this paper, we present CrafterDojo, a suite of foundat…

Investigating Polyglot Speech Foundation Models for Learning Collective Emotion from Crowds
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar, Panchal Nayak, Priyabrata Mallick, Swarup Ranjan Behera, Parabattina Bhagath, Pailla Balakrishna Reddy, Arun Balaji Buduru
https://arxiv.org/abs/2509.16329…

Investigating Polyglot Speech Foundation Models for Learning Collective Emotion from Crowds
This paper investigates the polyglot (multilingual) speech foundation models (SFMs) for Crowd Emotion Recognition (CER). We hypothesize that polyglot SFMs, pre-trained on diverse languages, accents, and speech patterns, are particularly adept at navigating the noisy and complex acoustic environments characteristic of crowd settings, thereby offering a significant advantage for CER. To substantiate this, we perform a comprehensive analysis, comparing polyglot, monolingual, and speaker recognitio…

Pixels Under Pressure: Exploring Fine-Tuning Paradigms for Foundation Models in High-Resolution Medical Imaging
Zahra TehraniNasab, Amar Kumar, Tal Arbel
https://arxiv.org/abs/2508.14931

Pixels Under Pressure: Exploring Fine-Tuning Paradigms for Foundation Models in High-Resolution Medical Imaging
Advancements in diffusion-based foundation models have improved text-to-image generation, yet most efforts have been limited to low-resolution settings. As high-resolution image synthesis becomes increasingly essential for various applications, particularly in medical imaging domains, fine-tuning emerges as a crucial mechanism for adapting these powerful pre-trained models to task-specific requirements and data distributions. In this work, we present a systematic study, examining the impact of …

Integration on $q$-Cosymplectic Manifolds
M. Leok, C. Sard\'on, X. Zhao
https://arxiv.org/abs/2509.16587 https://arxiv.org/pdf/2509.16587

Integration on $q$-Cosymplectic Manifolds
This paper presents a unified framework for studying dynamics and integration on $q$-cosymplectic manifolds. After outlining the geometric foundations of $q$-cosymplectic structures, we derive new results concerning integrable systems and the characterization of Liouville coordinates, and further investigate the Lie integrability of $q$-evolution systems in this setting. We then develop a Hamilton--Jacobi theory tailored to multi-time Hamiltonian systems, both from an intrinsic geometric perspe…

MesoNet: A Fundamental Principle for Multi-Representation Learning in Complex Chemical Systems
Jinming Fan, Chao Qian, Shaodong Zhou
https://arxiv.org/abs/2509.17810 https://

MesoNet: A Fundamental Principle for Multi-Representation Learning in Complex Chemical Systems
Accurate prediction of molecular properties in complex chemical systems is crucial for accelerating material discovery and chemical innovation.However,current computational methods often struggle to capture the intricate compositional interplay across complex chemical systems,from intramolecular bonds to intermolecular forces.In this work,we introduce MesoNet,a novel framework founded on the principle of multi-representation learning and specifically designed for multi-molecule modeling.The cor…

Fixed point results via a new class of Ad-contractions
Prasun Panthi, Dinesh Panthi
https://arxiv.org/abs/2507.15635 https://arxiv.or…

Fixed point results via a new class of Ad-contractions
In this article, a new class of operators, termed Ad-contractions, is introduced to extend the framework of A-contractions to the setting of dislocated metric spaces. Fixed point results are established for single mappings, sequences of mappings, integral type contractions, and for mappings on a set with two dislocated metrics. The demonstrated theorems generalize foundational results on A-contractions and their integral-type variations to the more challenging setting of dislocated metric space…

Mantis: A Simulation-Grounded Foundation Model for Disease Forecasting
Carson Dudley, Reiden Magdaleno, Christopher Harding, Ananya Sharma, Emily Martin, Marisa Eisenberg
https://arxiv.org/abs/2508.12260

Mantis: A Simulation-Grounded Foundation Model for Disease Forecasting
Infectious disease forecasting in novel outbreaks or low resource settings has been limited by the need for disease-specific data, bespoke training, and expert tuning. We introduce Mantis, a foundation model trained entirely on mechanistic simulations, which enables out-of-the-box forecasting across diseases, regions, and outcomes, even in settings with limited historical data. Mantis is built on over 400 million simulated days of outbreak dynamics spanning diverse pathogens, transmission modes…

... as it turns out the folks from R5 were knocking on my door just as I was setting up this shot: here's my new photo backpack and my new water bottle.
They had a bunch of abandoned water bottles at the Catherwood Library so I asked the clerk: "I work for an organization founded by Elmo Roper, can I have the bottle with Elmo?" and they gave it to me.
#sliceoflife…