Tootfinder

Source: the clause rescinding Microsoft's access to OpenAI's most powerful tech if OpenAI develops AGI remains part of their new deal, but it has been modified (New York Times)
https://www.nytimes.com/2025/09/11/technology/openai-microsoft-deal.html

OpenAI Takes Big Steps Toward Its Long-Planned Reorganization
The start-up reached a tentative deal with Microsoft, its biggest investor, and said it would give a $100 billion stake to the nonprofit that manages it.

Learning Obfuscations Of LLM Embedding Sequences: Stained Glass Transform
Jay Roberts, Kyle Mylonakis, Sidhartha Roy, Kaan Kale
https://arxiv.org/abs/2506.09452

Learning Obfuscations Of LLM Embedding Sequences: Stained Glass Transform
The high cost of ownership of AI compute infrastructure and challenges of robust serving of large language models (LLMs) has led to a surge in managed Model-as-a-service deployments. Even when enterprises choose on-premises deployments, the compute infrastructure is typically shared across many teams in order to maximize the return on investment. In both scenarios the deployed models operate only on plaintext data, and so enterprise data owners must allow their data to appear in plaintext on a …

A Pathway to Practical Quantum Advantage in Solving Navier-Stokes Equations
Xi-Ning Zhuang, Zhao-Yun Chen, Ming-Yang Tan, Jiaxuan Zhang, Chuang-Chao Ye, Tian-Hao Wei, Teng-Yang Ma, Cheng Xue, Huan-Yu Liu, Qing-Song Li, Tai-Ping Sun, Xiao-Fan Xu, Yun-Jie Wang, Yu-Chun Wu, Guo-Ping Guo
https://arxiv.org/abs/2509.08807

A Pathway to Practical Quantum Advantage in Solving Navier-Stokes Equations
The advent of fault-tolerant quantum computing (FTQC) promises to tackle classically intractable problems. A key milestone is solving the Navier-Stokes equations (NSE), which has remained formidable for quantum algorithms due to their high input-output overhead and nonlinearity. Here, we establish a full-stack framework that charts a practical pathway to a quantum advantage for large-scale NSE simulation. Our approach integrates a spectral-based input/output algorithm, an explicit and synthesiz…

Modular Vacuum-Based Fixturing System for Adaptive Disassembly Workspace Integration
Haohui Pan, Takuya Kiyokawa, Tomoki Ishikura, Shingo Hamada, Genichiro Matsuda, Kensuke Harada
https://arxiv.org/abs/2508.05936

Modular Vacuum-Based Fixturing System for Adaptive Disassembly Workspace Integration
The disassembly of small household appliances poses significant challenges due to their complex and curved geometries, which render traditional rigid fixtures inadequate. In this paper, we propose a modular vacuum-based fixturing system that leverages commercially available balloon-type soft grippers to conform to arbitrarily shaped surfaces and provide stable support during screw-removal tasks. To enable a reliable deployment of the system, we develop a stability-aware planning framework that …

Fiducial-Cosmology-dependent systematics for the DESI 2024 Full-Shape Analysis
R. Gsponer, S. Ramirez-Solano, F. Rodr\'iguez-Mart\'inez, M. Vargas-Maga\~na, S. Novell-Masot, N. Findlay, H. Gil-Mar\'in, P. Zarrouk, S. Nadathur, A. Rocher, S. Brieden, A. P\'erez-Fern\'andez, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, F. J. Castander, T. Claybaugh, A. Cuceu, A. de la Macorra, A. de Mattia, Arjun Dey, P. Doel, A. Font-Ribera, J. E. Forero-Romero, E. Gazta\~naga, S. Go…

Fiducial-Cosmology-dependent systematics for the DESI 2024 Full-Shape Analysis
We assess the impact of the fiducial cosmology choice on cosmological inference from full-shape (FS) fits of the galaxy power spectrum in the DESI 2024 Data Release 1 (DR1). Using a suite of AbacusSummit DR1 mock catalogues based on the Planck 2018 best-fit cosmology, we quantify potential systematic shifts introduced by analysing the data under five secondary cosmologies - featuring variations in matter density, thawing dark energy, higher effective number of neutrino species, reduced clusteri…

ASLSL: Adaptive shared latent structure learning with incomplete multi-modal physiological data for multi-dimensional emotional feature selection
Xueyuan Xu, Tianze Yu, Wenjia Dong, Fulin Wei, Li Zhuo
https://arxiv.org/abs/2508.05934

ASLSL: Adaptive shared latent structure learning with incomplete multi-modal physiological data for multi-dimensional emotional feature selection
Recently, multi-modal physiological signals based emotion recognition has garnered increasing attention in the field of brain-computer interfaces. Nevertheness, the associated multi-modal physiological features are often high-dimensional and inevitably include irrelevant, redundant, and noisy representation, which can easily lead to overfitting, poor performance, and high computational complexity in emotion classifiers. Feature selection has been widely applied to address these challenges. Howe…

The best microcontroller thing I did last year was build this little kit that fits in a 3D printed rugged box and covers close to 90% of things I need to deal with for the products I sell & support.
I can easily throw it in my bag when I'm on the road, otherwise it sits on my desk where I can quickly grab it to test something.
#maker

SHAining on Process Mining: Explaining Event Log Characteristics Impact on Algorithms
Andrea Maldonado, Christian M. M. Frey, Sai Anirudh Aryasomayajula, Ludwig Zellner, Stephan A. Fahrenkrog-Petersen, Thomas Seidl
https://arxiv.org/abs/2509.08482

SHAining on Process Mining: Explaining Event Log Characteristics Impact on Algorithms
Process mining aims to extract and analyze insights from event logs, yet algorithm metric results vary widely depending on structural event log characteristics. Existing work often evaluates algorithms on a fixed set of real-world event logs but lacks a systematic analysis of how event log characteristics impact algorithms individually. Moreover, since event logs are generated from processes, where characteristics co-occur, we focus on associational rather than causal effects to assess how stro…

Building Open-Retrieval Conversational Question Answering Systems by Generating Synthetic Data and Decontextualizing User Questions
Christos Vlachos, Nikolaos Stylianou, Alexandra Fiotaki, Spiros Methenitis, Elisavet Palogiannidi, Themos Stafylakis, Ion Androutsopoulos
https://arxiv.org/abs/2507.04884

Building Open-Retrieval Conversational Question Answering Systems by Generating Synthetic Data and Decontextualizing User Questions
We consider open-retrieval conversational question answering (OR-CONVQA), an extension of question answering where system responses need to be (i) aware of dialog history and (ii) grounded in documents (or document fragments) retrieved per question. Domain-specific OR-CONVQA training datasets are crucial for real-world applications, but hard to obtain. We propose a pipeline that capitalizes on the abundance of plain text documents in organizations (e.g., product documentation) to automatically …

Theropod tracks "of a very large dinosaur for which we currently have no skeletal evidence." Mill Canyon site, near Moab, Utah #FossilFriday