Tootfinder

Function-on-function Differential Regression
Tongyu Li, Fang Yao
https://arxiv.org/abs/2506.02363 https://arxiv.org/pdf/2506.02363

Function-on-function Differential Regression
Function-on-function regression has been a topic of substantial interest due to its broad applicability, where the relation between functional predictor and response is concerned. In this article, we propose a new framework for modeling the regression mapping that extends beyond integral type, motivated by the prevalence of physical phenomena governed by differential relations, which is referred to as function-on-function differential regression. However, a key challenge lies in representing th…

Functionality Assessment Framework for Autonomous Driving Systems using Subjective Networks
Stefan Orf, Sven Ochs, Valentin Marotta, Oliver Conder, Marc Ren\'e Zofka, J. Marius Z\"ollner
https://arxiv.org/abs/2506.02922

Functionality Assessment Framework for Autonomous Driving Systems using Subjective Networks
In complex autonomous driving (AD) software systems, the functioning of each system part is crucial for safe operation. By measuring the current functionality or operability of individual components an isolated glimpse into the system is given. Literature provides several of these detached assessments, often in the form of safety or performance measures. But dependencies, redundancies, error propagation and conflicting functionality statements do not allow for easy combination of these measures…

The Washington Post could soon allow non-professional writers to submit opinion columns using an AI writing coach known as Ember,
according to a report from The New York Times.
The move is reportedly part of a broader initiative to open the paper to outside opinion pieces, including from other publications, Substack writers, and amateur columnists.
Sources tell the Times that Ember “could automate several functions normally provided by human editors,”
including by offe…

The Washington Post is planning to let amateur writers submit columns — with the help of AI
The Washington Post is planning a new initiative that will allow non-professional writers to submit opinion pieces using an AI-powered writing coach, called Ember, according to The New York Times.

Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
Mahmud Ashraf Shamim, Eric Reinhardt, Talal Ahmed Chowdhury, Sergei Gleyzer, Paulo T Araujo
https://arxiv.org/abs/2506.01891

Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
Neural Quantum States (NQS) are a class of variational wave functions parametrized by neural networks (NNs) to study quantum many-body systems. In this work, we propose SineKAN, the NQS ansatz based on Kolmogorov-Arnold Networks (KANs), to represent quantum mechanical wave functions as nested univariate functions. We show that \sk wavefunction with learnable sinusoidal activation functions can capture the ground state energies, fidelities and various correlation functions of the 1D Transverse-F…

Seriously, linkedin spammers?
You say you came across my REcon talk. About semiconductor reverse engineering.
And this somehow makes me a good candidate for a role involving cheating at online poker???

@… Yeah, I agree. I was also disappointed; I thought the alternative proposals in the thread (such as `<embed>` with MIME types) made a lot more sense, and it just culminates in Andreessen saying “fuck you, we’re doing what I proposed”.
And now we’re stuck with it. Thanks, Marc.

$IF-GUIDE$: Influence Function-Guided Detoxification of LLMs
Zachary Coalson, Juhan Bae, Nicholas Carlini, Sanghyun Hong
https://arxiv.org/abs/2506.01790 h…

$IF-GUIDE$: Influence Function-Guided Detoxification of LLMs
We study how training data contributes to the emergence of toxic behaviors in large-language models. Most prior work on reducing model toxicity adopts $reactive$ approaches, such as fine-tuning pre-trained (and potentially toxic) models to align them with human values. In contrast, we propose a $proactive$ approach$-$IF-Guide$-$which leverages influence functions to identify harmful tokens within any training data and suppress their impact during training. To this end, we first show that standa…

Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model
Javier Salazar Cavazos, Scott Peltier
https://arxiv.org/abs/2506.02060

Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model
Previous works in the literature apply 3D spatial-only models on 4D functional MRI data leading to possible sub-par feature extraction to be used for downstream tasks like classification. In this work, we aim to develop a novel 4D convolution network to extract 4D joint temporal-spatial kernels that not only learn spatial information but in addition also capture temporal dynamics. Experimental results show promising performance in capturing spatial-temporal data in functional MRI compared to 3D…

Variable Selection in Functional Linear Cox Model
Yuanzhen Yue, Stella Self, Yichao Wu, Jiajia Zhang, Rahul Ghosal
https://arxiv.org/abs/2506.02524 https:/…

Variable Selection in Functional Linear Cox Model
Modern biomedical studies frequently collect complex, high-dimensional physiological signals using wearables and sensors along with time-to-event outcomes, making efficient variable selection methods crucial for interpretation and improving the accuracy of survival models. We propose a novel variable selection method for a functional linear Cox model with multiple functional and scalar covariates measured at baseline. We utilize a spline-based semiparametric estimation approach for the function…

This https://arxiv.org/abs/2502.16888 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Functional BART with Shape Priors: A Bayesian Tree Approach to Constrained Functional Regression
Motivated by the remarkable success of Bayesian additive regression trees (BART) in regression modelling, we propose a novel nonparametric Bayesian method, termed Functional BART (FBART), tailored specifically for function-on-scalar regression. FBART leverages spline-based representations for functional responses coupled with a flexible tree-based partitioning structure, effectively capturing complex and heterogeneous relationships between response curves and scalar predictors. To facilitate ef…