Tootfinder

Mediatek entwirft angeblich Notebook-Prozessor mit Nvidia-Technik
Mediatek und Nvidia wollen offenbar AMD, Intel und Qualcomm bei Notebooks Konkurrenz machen. Dazu soll eine ARM-CPU Nvidia-Technik integrieren.

If you use excel to grade papers and exams, maybe this site will save you hours (days?) of effort like it just did me:
https://www.exceldemy.com/split-excel-sheet-into-multiple-files/

The Methods of Moral Panic Journalism https://michaelhobbes.substack.com/p/moral-panic-journalism

The Methods of Moral Panic Journalism
Scare stories on "left-wing illiberalism" display a familiar pattern.

Numerical Discretization Methods for Linear Quadratic Control Problems with Time Delays
Zhanhao Zhang, Steen H{\o}rsholt, John Bagterp J{\o}rgensen
https://arxiv.org/abs/2404.08440

Numerical Discretization Methods for Linear Quadratic Control Problems with Time Delays
This paper presents the numerical discretization methods of the continuous-time linear-quadratic optimal control problems (LQ-OCPs) with time delays. We describe the weight matrices of the LQ-OCPs as differential equations systems, allowing us to derive the discrete equivalent of the continuous-time LQ-OCPs. Three numerical methods are introduced for solving proposed differential equations systems: 1) the ordinary differential equation (ODE) method, 2) the matrix exponential method, and 3) the …

iJTyper: An Iterative Type Inference Framework for Java by Integrating Constraint- and Statistically-based Methods
Zhixiang Chen, Anji Li, Neng Zhang, Jianguo Chen, Yuan Huang, Zibin Zheng
https://arxiv.org/abs/2402.09995

iJTyper: An Iterative Type Inference Framework for Java by Integrating Constraint- and Statistically-based Methods
Inferring the types of API elements in incomplete code snippets (e.g., those on Q&A forums) is a prepositive step required to work with the code snippets. Existing type inference methods can be mainly categorized as constraint-based or statistically-based. The former imposes higher requirements on code syntax and often suffers from low recall due to the syntactic limitation of code snippets. The latter relies on the statistical regularities learned from a training corpus and does not take full …

This https://arxiv.org/abs/2307.11157 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
Machine learning--based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant anomaly detection, where the BSM is assumed to be localized in at least one known variable. While there have been many methods proposed to identify such a BSM signal that make use of simulated or detected data in different ways, there has not yet been a study of t…

This https://arxiv.org/abs/2211.06410 has been replaced.
link: https://scholar.google.com/scholar?q=a

RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features
Kernel methods provide a flexible and theoretically grounded approach to nonlinear and nonparametric learning. While memory and run-time requirements hinder their applicability to large datasets, many low-rank kernel approximations, such as random Fourier features, were recently developed to scale up such kernel methods. However, these scalable approaches are based on approximations of isotropic kernels, which cannot remove the influence of irrelevant features. In this work, we design random Fo…

Consistency analysis of refined instrumental variable methods for continuous-time system identification in closed-loop
Rodrigo A. Gonz\'alez, Siqi Pan, Cristian R. Rojas, James S. Welsh
https://arxiv.org/abs/2404.08955

Consistency analysis of refined instrumental variable methods for continuous-time system identification in closed-loop
Refined instrumental variable methods have been broadly used for identification of continuous-time systems in both open and closed-loop settings. However, the theoretical properties of these methods are still yet to be fully understood when operating in closed-loop. In this paper, we address the consistency of the simplified refined instrumental variable method for continuous-time systems (SRIVC) and its closed-loop variant CLSRIVC when they are applied on data that is generated from a feedback…

This https://arxiv.org/abs/2211.06410 has been replaced.
link: https://scholar.google.com/scholar?q=a

RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features
Kernel methods provide a flexible and theoretically grounded approach to nonlinear and nonparametric learning. While memory and run-time requirements hinder their applicability to large datasets, many low-rank kernel approximations, such as random Fourier features, were recently developed to scale up such kernel methods. However, these scalable approaches are based on approximations of isotropic kernels, which cannot remove the influence of irrelevant features. In this work, we design random Fo…

This https://arxiv.org/abs/2308.14142 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes
Sparse variational approximations are popular methods for scaling up inference and learning in Gaussian processes to larger datasets. For $N$ training points, exact inference has $O(N^3)$ cost; with $M \ll N$ features, state of the art sparse variational methods have $O(NM^2)$ cost. Recently, methods have been proposed using more sophisticated features; these promise $O(M^3)$ cost, with good performance in low dimensional tasks such as spatial modelling, but they only work with a very limited c…