Tootfinder

Power Transform Revisited: Numerically Stable, and Federated
Xuefeng Xu, Graham Cormode
https://arxiv.org/abs/2510.04995 https://arxiv.org/pdf/2510.04995…

Power Transform Revisited: Numerically Stable, and Federated
Power transforms are popular parametric techniques for making data more Gaussian-like, and are widely used as preprocessing steps in statistical analysis and machine learning. However, we find that direct implementations of power transforms suffer from severe numerical instabilities, which can lead to incorrect results or even crashes. In this paper, we provide a comprehensive analysis of the sources of these instabilities and propose effective remedies. We further extend power transforms to th…

A New Partial State-Feedback IDA-PBC for Two-Dimensional Nonlinear Systems: Application to Power Converters with Experimental Results
Rafael Cisneros, Leyan Fang, Wei He, Romeo Ortega
https://arxiv.org/abs/2510.01425

A New Partial State-Feedback IDA-PBC for Two-Dimensional Nonlinear Systems: Application to Power Converters with Experimental Results
In this paper we propose a variation of the widely popular Interconnection-and-Damping-Assigment Passivity-Based Control (IDA-PBC) based on Poincare's Lemma to design output feedback globally stabilizing controllers for two dimensional systems. The procedure is constructive and, in comparison with the classical IDA-PBC, whose application is often stymied by the need to solve the (infamous) matching partial differential equation (PDE), in this new method the PDE is replaced by an ordinary differ…

PPD-CPP: Pointwise predictive density calibrated-power prior in dynamically borrowing historical information
Shixuan Wang, Jing Zhang, Emily L. Kang, Bin Zhang
https://arxiv.org/abs/2509.25688

PPD-CPP: Pointwise predictive density calibrated-power prior in dynamically borrowing historical information
Incorporating historical or real-world data into analyses of treatment effects for rare diseases has become increasingly popular. A major challenge, however, lies in determining the appropriate degree of congruence between historical and current data. In this study, we devote ourselves to the capacity of historical data in replicating the current data, and propose a new congruence measure/estimand $p_{CM}$. $p_{CM}$ quantifies the heterogeneity between two datasets following the idea of the mar…

USpol
Was just having a conversation with someone about how "50%" of the country voted for Trump and in reminding them it's way less took the time to look up the numbers. In 2024 Trump got 77.3 million votes in a country of 340.1 million people. That's 22.7% of the people. ~21.5% of the people are under age 18, so 263.6 million adults. Trump got the vote of 29.3% of them. Some of those adults aren't eligible to vote, but when discussing social dynamics, they matter just as much, as do kids... Even of the people who voted for Trump, many were not at all MAGA fanatics (even if they at minimum had a seriously warped worldview and dangerous politics of one kind or another).
The truth is, almost no president has ever had a huge popular mandate, because our society is engineered to let a minority political class retain most of the power while giving the illusion of influence to the masses.

Generalized Mandelstam-Leibbrandt regularization
Jorge Alfaro
https://arxiv.org/abs/2510.01200 https://arxiv.org/pdf/2510.01200

Generalized Mandelstam-Leibbrandt regularization
Algebraic non-covariant gauges are used often in string theory, Chern-Simons theory, gravitation and gauge theories. Loop integrals, however, have spurious singularities that need to be regularized. The most popular and consistent regularization is the Mandelstam- Leibbrandt(ML) prescription. This paper extends the ML prescription outside the light cone. It shares all the properties of light-cone ML regularization: It preserves naive power counting and gauge invariance. Moreover, using di…

Chargers' new 'Charger Power' alternate, Lions' all-black lead NFL Week 7 uniforms https://www.espn.com/nfl/story/_/id/46597978/nfl-2025-week-7-uniforms

A Steered Response Power Method for Sound Source Localization With Generic Acoustic Models
Kaspar M\"uller, Markus Buck, Simon Doclo, Jan {\O}stergaard, Tobias Wolff
https://arxiv.org/abs/2509.15702

A Steered Response Power Method for Sound Source Localization With Generic Acoustic Models
The steered response power (SRP) method is one of the most popular approaches for acoustic source localization with microphone arrays. It is often based on simplifying acoustic assumptions, such as an omnidirectional sound source in the far field of the microphone array(s), free field propagation, and spatially uncorrelated noise. In reality, however, there are many acoustic scenarios where such assumptions are violated. This paper proposes a generalization of the conventional SRP method that a…

Data-driven modeling of wind farm wake flow based on multi-scale feature recognition
Dong Xu, Zhaobin Li, Xiaolei Yang, Peng Hou, Bruno Carmo, Xuerui Mao
https://arxiv.org/abs/2509.21005

Data-driven modeling of wind farm wake flow based on multi-scale feature recognition
Accurate, efficient prediction of wind flow with wake effects is crucial for wind-farm layout and power forecasting. Existing approaches-physical measurements, numerical simulations, physics-based models, and data-driven models-face trade-offs: the first two are time- and resource-intensive; physics-based models can lack accuracy due to limited physics; data-driven methods leverage abundant, high-quality data and are increasingly popular. We propose a rapid, data-driven wake-flow model inspired…