Tootfinder

Hey, #DeltaAirlines, #AmericanAirlines, and #UnitedAirlines: selling passenger data to the Feds without requiring a warrant, are you?

Kibble-Zurek dynamical scaling hypothesis in the Google analog-digital quantum simulator of the $XX$ model
Yintai Zhang, Francis A. Bayocboc Jr., Jacek Dziarmaga
https://arxiv.org/abs/2506.10771

Kibble-Zurek dynamical scaling hypothesis in the Google analog-digital quantum simulator of the $XX$ model
The state-of-the-art tensor networks are employed to simulate the Hamiltonian ramp in the analog-digital quantum simulation of the quantum phase transition to the quasi-long-range ordered phase of the 2D square-lattice $XX$ model [Nature 638, 79 (2025)]. We focus on the quantum Kibble-Zurek (KZ) mechanism near the quantum critical point. Using the infinite projected entangled pair state (iPEPS), we simulate an infinite lattice and demonstrate the KZ scaling hypothesis for the $XX$ correlations …

Complex scaling for open waveguides
Charles L. Epstein, Tristan Goodwill, Jeremy Hoskins, Solomon Quinn, Manas Rachh
https://arxiv.org/abs/2506.10263 https…

Complex scaling for open waveguides
In this work we analyze the complex scaling method applied to the problem of time-harmonic scalar wave propagation in junctions between `leaky,' or open dielectric waveguides. In [arXiv:2302.04353, arXiv:2310.05816, arXiv:2401.04674, arXiv:2411.11204], it was shown that under suitable assumptions the problem can be reduced to a system of Fredholm second-kind integral equations on an infinite interface, transverse to the waveguides. Here, we show that the kernels appearing in the integral equati…

Strong article about "the silence of the generals"
#usa

The Silence of the Generals
As President Donald Trump crossed a dangerous line at Fort Bragg, the brass failed to speak out in the Army’s defense.

Scaling Laws for Uncertainty in Deep Learning
Mattia Rosso, Simone Rossi, Giulio Franzese, Markus Heinonen, Maurizio Filippone
https://arxiv.org/abs/2506.09648

Scaling Laws for Uncertainty in Deep Learning
Deep learning has recently revealed the existence of scaling laws, demonstrating that model performance follows predictable trends based on dataset and model sizes. Inspired by these findings and fascinating phenomena emerging in the over-parameterized regime, we examine a parallel direction: do similar scaling laws govern predictive uncertainties in deep learning? In identifiable parametric models, such scaling laws can be derived in a straightforward manner by treating model parameters in a B…

Break-up of an active chiral fluid
Luke Neville, Jens Eggers, Tanniemola B. Liverpool
https://arxiv.org/abs/2506.10534 https://arxiv.…

Break-up of an active chiral fluid
We consider the non-linear dynamics governing the break-up of a two-dimensional strip of active chiral fluid. We observe that the strip thickness goes to zero at the pinch off points as a power law in finite time. Using slender body theory combined with a scaling analysis, we identify a new class of scaling exponents and scaling functions characterizing the speed and shape of break-up in these systems. The scaling analysis is in excellent agreement with direct numerical simulations of the hydro…

Towards Understanding Bugs in Distributed Training and Inference Frameworks for Large Language Models
Xiao Yu, Haoxuan Chen, Feifei Niu, Xing Hu, Jacky Wai Keung, Xin Xia
https://arxiv.org/abs/2506.10426

Towards Understanding Bugs in Distributed Training and Inference Frameworks for Large Language Models
With the rapid development of large language models (LLMs), distributed training and inference frameworks like DeepSpeed have become essential for scaling model training and inference across multiple GPUs or nodes. However, the increasing complexity of these frameworks brings non-trivial software bugs, which may degrade training performance, cause unexpected failures, and result in significant resource waste. Understanding framework bugs' characteristics is fundamental for quality assurance, al…

Human-robot collaborative transport personalization via Dynamic Movement Primitives and velocity scaling
Paolo Franceschi, Andrea Bussolan, Vincenzo Pomponi, Oliver Avram, Stefano Baraldo, Anna Valente
https://arxiv.org/abs/2506.09697

Human-robot collaborative transport personalization via Dynamic Movement Primitives and velocity scaling
Nowadays, industries are showing a growing interest in human-robot collaboration, particularly for shared tasks. This requires intelligent strategies to plan a robot's motions, considering both task constraints and human-specific factors such as height and movement preferences. This work introduces a novel approach to generate personalized trajectories using Dynamic Movement Primitives (DMPs), enhanced with real-time velocity scaling based on human feedback. The method was rigorously tested in …

Critical scaling for spectral functions
Konrad Kockler, Jan M. Pawlowski, Jonas Wessely
https://arxiv.org/abs/2506.09142 https://arxi…

Critical scaling for spectral functions
We study real-time scalar $ϕ^4$-theory in 2+1 dimensions near criticality. Specifically, we compute the single-particle spectral function and that of the $s$-channel four-point function in and outside the scaling regime. The computation is done with the spectral functional Callan-Symanzik equation, which exhibits manifest Lorentz invariance and preserves causality. We extract the scaling exponent $η$ from the spectral function and compare our result with that from a Euclidean fixed point anal…

GPU Acceleration of SQL Analytics on Compressed Data
Zezhou Huang, Krystian Sakowski, Hans Lehnert, Wei Cui, Carlo Curino, Matteo Interlandi, Marius Dumitru, Rathijit Sen
https://arxiv.org/abs/2506.10092

GPU Acceleration of SQL Analytics on Compressed Data
GPUs are uniquely suited to accelerate (SQL) analytics workloads thanks to their massive compute parallelism and High Bandwidth Memory (HBM) -- when datasets fit in the GPU HBM, performance is unparalleled. Unfortunately, GPU HBMs remain typically small when compared with lower-bandwidth CPU main memory. Besides brute-force scaling across many GPUs, current solutions to accelerate queries on large datasets include leveraging data partitioning and loading smaller data batches in GPU HBM, and hyb…