Tootfinder

This https://arxiv.org/abs/2412.17629 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Learn from Global Correlations: Enhancing Evolutionary Algorithm via Spectral GNN
Evolutionary algorithms (EAs) simulate natural selection but have two main limitations: (1) they rarely update individuals based on global correlations, limiting comprehensive learning; (2) they struggle with balancing exploration and exploitation, where excessive exploitation causes premature convergence, and excessive exploration slows down the search. Moreover, EAs often depend on manual parameter settings, which can disrupt the exploration-exploitation balance.To address these issues, we pr…

This https://arxiv.org/abs/2412.17629 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Learn from Global Correlations: Enhancing Evolutionary Algorithm via Spectral GNN
Evolutionary algorithms (EAs) simulate natural selection but have two main limitations: (1) they rarely update individuals based on global correlations, limiting comprehensive learning; (2) they struggle with balancing exploration and exploitation, where excessive exploitation causes premature convergence, and excessive exploration slows down the search. Moreover, EAs often depend on manual parameter settings, which can disrupt the exploration-exploitation balance.To address these issues, we pr…

The Hyper-Kamiokande experiment: input to the update of the European Strategy for Particle Physics
Kamiokande Collaboration
https://arxiv.org/abs/2506.16641

The Hyper-Kamiokande experiment: input to the update of the European Strategy for Particle Physics
Hyper-Kamiokande is a large infrastructure for particle and astroparticle physics being built in Japan and aiming to start operations by the end of 2027 whose objective is to address the most important questions in science today, for instance how the universe began and evolved. It aims to measure with the highest precision the leptonic Charge-Parity violation parameter using both a Mega-Watt intense neutrino beam and high-statistics atmospheric neutrino samples. The combination of these sampl…

Uncertainty Prioritized Experience Replay
Rodrigo Carrasco-Davis, Sebastian Lee, Claudia Clopath, Will Dabney
https://arxiv.org/abs/2506.09270 https://

Uncertainty Prioritized Experience Replay
Prioritized experience replay, which improves sample efficiency by selecting relevant transitions to update parameter estimates, is a crucial component of contemporary value-based deep reinforcement learning models. Typically, transitions are prioritized based on their temporal difference error. However, this approach is prone to favoring noisy transitions, even when the value estimation closely approximates the target mean. This phenomenon resembles the noisy TV problem postulated in the explo…

I have published an update to the 'Add Arguments to Google Searches' Firefox extension which permits appending the &udm=14 parameter to the results URL, fully eliminating all AI slop as well as any other hint of machine 'helpfulness' in Google results. The extension will also now install and run on both Firefox and Firefox for Android.

Fully Bayesian Sequential Design for Mean Response Surface Prediction of Heteroscedastic Stochastic Simulations
Yuying Huang, Samuel W. K. Wong
https://arxiv.org/abs/2506.09722

Fully Bayesian Sequential Design for Mean Response Surface Prediction of Heteroscedastic Stochastic Simulations
We present a fully Bayesian sequential strategy for predicting the mean response surface of heteroscedastic stochastic simulation functions. Leveraging dual Gaussian processes as the surrogate model and a criterion based on empirical expected integrated mean-square prediction error, our approach sequentially selects informative design points while fully accounting for parameter uncertainty. Sequential importance sampling is employed to efficiently update the posterior distribution of the parame…

MLorc: Momentum Low-rank Compression for Large Language Model Adaptation
Wei Shen, Yaxiang Zhang, Minhui Huang, Mengfan Xu, Jiawei Zhang, Cong Shen
https://arxiv.org/abs/2506.01897

MLorc: Momentum Low-rank Compression for Large Language Model Adaptation
With increasing size of large language models (LLMs), full-parameter fine-tuning imposes substantial memory demands. To alleviate this, we propose a novel memory-efficient training paradigm called Momentum Low-rank compression (MLorc). By directly compressing and reconstructing momentum rather than gradients, MLorc avoids imposing a fixed-rank constraint on weight update matrices and better preserves the training dynamics of full-parameter fine-tuning, in contrast to existing low-rank approache…

This https://arxiv.org/abs/2505.03834 has been replaced.
initial toot: https://mastoxiv.page/@a…

Update: Progress toward fusion energy breakeven and gain as measured against the Lawson criteria
This paper is an update to our earlier paper ''Progress toward fusion energy breakeven and gain as measured against the Lawson criterion'' [Phys. Plasmas 29, 062103 (2022)]. Plots of Lawson parameter and triple product vs. ion temperature and triple product vs. date achieved are updated with recently published experimental results. A new plot of scientific energy gain vs. date achieved is included. Additionally, notes on new experimental results, clarifications, and a correction are included.