Tootfinder

Emergent Quasiparticles \& Field-Tuned RIXS Spectra in a Trimerized Spin-1/2 Chain
Subhajyoti Pal, Pradeep Thakur, Ashis Kumar Nandy, Anamitra Mukherjee
https://arxiv.org/abs/2505.23208

Emergent Quasiparticles \& Field-Tuned RIXS Spectra in a Trimerized Spin-1/2 Chain
We investigate spin-flip excitations in the spin-1/2 trimer chain $\rm{Cu_3(P_2O_6OH)_2}$, featuring an antiferromagnetic exchange motif $J_1$-$J_1$-$J_2$ with $J_1 < J_2$. Using density matrix renormalization group (DMRG) simulations, we demonstrate that single-spin-flip processes induced by resonant inelastic X-ray scattering (RIXS) generate emergent gapless modes governed by the underlying trimer periodicity alongside distinct high-energy excitations. By combining exact diagonalization and r…

PSA: 🥵 Idk who needs to hear this, but in a heatwave for the love of science use air conditioning. If you don’t have air conditioning, get a portable unit as soon as possible.
The climate is going to get hotter and heat can very easily kill you or a loved one or a pet; or cause permanent disability.
Heat is a lot more dangerous than cold temperatures; even if you’re “young and healthy”.
The climate is already fucked and has long reached a tipping point as far as humans go—our only bet is large-scale CO2 extraction.
Sacrificing your health “to save energy*” for an imaginary fight that we have already lost is stupid. It’s also one of those “individual responsibility” mindfucks—the culprits for pollution are industry maximizing profits and governments that are not acting.
Anyway, stay cool.
*note that AC uses overall a lot less energy than heating does

Can LLMs Replace Humans During Code Chunking?
Christopher Glasz, Emily Escamilla, Eric O. Scott, Anand Patel, Jacob Zimmer, Colin Diggs, Michael Doyle, Scott Rosen, Nitin Naik, Justin F. Brunelle, Samruddhi Thaker, Parthav Poudel, Arun Sridharan, Amit Madan, Doug Wendt, William Macke, Thomas Schill
https://arxiv.org/abs/2506.198…

Can LLMs Replace Humans During Code Chunking?
Large language models (LLMs) have become essential tools in computer science, especially for tasks involving code understanding and generation. However, existing work does not address many of the unique challenges presented by code written for government applications. In particular, government enterprise software is often written in legacy languages like MUMPS or assembly language code (ALC) and the overall token lengths of these systems exceed the context window size for current commercially a…

The Midas Project and Tech Oversight Project release The OpenAI Files, a 50 page analysis of OpenAI's governance, leadership, and organizational culture (Hayden Field/The Verge)
https://www.theverge.com/openai/688783/the

The ‘OpenAI Files’ will help you understand how Sam Altman’s company works
Two tech industry watchdog organizations released a research report called the OpenAI Files that covers OpenAI’s corporate structure, its restructuring to a for-profit organization, CEO Sam Altman’s portfolio of investment companies, and more.

Understanding the Error Sensitivity of Privacy-Aware Computing
Mat\'ias Mazzanti (University of Buenos Aires), Esteban Mocskos (University of Buenos Aires), Augusto Vega (IBM T. J. Watson Research Center), Pradip Bose (IBM T. J. Watson Research Center)
https://arxiv.org/abs/2506.07957

Understanding the Error Sensitivity of Privacy-Aware Computing
Homomorphic Encryption (HE) enables secure computation on encrypted data without decryption, allowing a great opportunity for privacy-preserving computation. In particular, domains such as healthcare, finance, and government, where data privacy and security are of utmost importance, can benefit from HE by enabling third-party computation and services on sensitive data. In other words, HE constitutes the "Holy Grail" of cryptography: data remains encrypted all the time, being protected while in …

Bayesian Generalized Nonlinear Models Offer Basis Free SINDy With Model Uncertainty
Aliaksandr Hubin
https://arxiv.org/abs/2507.06776 https://

Bayesian Generalized Nonlinear Models Offer Basis Free SINDy With Model Uncertainty
Sparse Identification of Nonlinear Dynamics (SINDy) has become a standard methodology for inferring governing equations of dynamical systems from observed data using statistical modeling. However, classical SINDy approaches rely on predefined libraries of candidate functions to model nonlinearities, which limits flexibility and excludes robust uncertainty quantification. This paper proposes Bayesian Generalized Nonlinear Models (BGNLMs) as a principled alternative for more flexible statistical …

Neural Entropy-stable conservative flux form neural networks for learning hyperbolic conservation laws
Lizuo Liu, Lu Zhang, Anne Gelb
https://arxiv.org/abs/2507.01795

Neural Entropy-stable conservative flux form neural networks for learning hyperbolic conservation laws
We propose a neural entropy-stable conservative flux form neural network (NESCFN) for learning hyperbolic conservation laws and their associated entropy functions directly from solution trajectories, without requiring any predefined numerical discretization. While recent neural network architectures have successfully integrated classical numerical principles into learned models, most rely on prior knowledge of the governing equations or assume a fixed discretization. Our approach removes this d…