Tootfinder

This https://arxiv.org/abs/2404.15996 has been replaced.
link: https://scholar.google.com/scholar?q=a

Asymptotically Fair and Truthful Allocation of Public Goods
We study the fair and truthful allocation of m divisible public items among n agents, each with distinct preferences for the items. To aggregate agents' preferences fairly, we focus on finding a core solution. For divisible items, a core solution always exists and can be calculated by maximizing the Nash welfare objective. However, such a solution is easily manipulated; agents might have incentives to misreport their preferences. To mitigate this, the current state-of-the-art finds an approxima…

This https://arxiv.org/abs/2409.19080 has been replaced.
link: https://scholar.google.com/scholar?q=a

Stimulated Raman Spectroscopy with Tunable Visible Broadband Probe Pulse Generated by Kerr-Instability Amplification
Femtosecond, broadband stimulated Raman spectroscopy is a popular approach to measuring molecular dynamics with excellent signal-to-noise and spectral resolution. We present a new method for broadband stimulated Raman spectroscopy that employs Kerr-instability amplification to amplify the supercontinuum spectrum from sapphire and create a highly tunable Raman probe spectrum spanning from 530 to 1000 nm (-6000 to 2800 cm^(-1)). Our method, called Kerr-instability amplification for broadband stim…

This https://arxiv.org/abs/2402.01593 has been replaced.
link: https://scholar.google.com/scholar?q=a

Statistical Accuracy of Approximate Filtering Methods
Estimating the statistics of the state of a dynamical system, from partial and noisy observations, is both mathematically challenging and finds wide application. Furthermore, the applications are of great societal importance, including problems such as probabilistic weather forecasting and prediction of epidemics. Particle filters provide a well-founded approach to the problem, leading to provably accurate approximations of the statistics. However these methods perform poorly in high dimensions…