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Vitamin C flips your skin’s “youth genes,” reversing age-related thinning #nutrition

Vitamin C flips your skin’s “youth genes,” reversing age-related thinning
Japanese researchers have found that vitamin C can thicken skin by switching on genes that boost skin cell growth, helping reverse age-related thinning. It works by reactivating DNA through a process that lets cells regenerate more effectively—potentially a game-changer for aging skin.

Digital and Robotic Twinning for Validation of Proximity Operations and Formation Flying
Aviad Golan, Gregory Zin, Zahra Ahmed, Emily Bates, Toby Bell, Pol Francesch Huc, Samuel Y. W. Low, Juergen Bosse, Simone D'Amico
https://arxiv.org/abs/2507.20034

Digital and Robotic Twinning for Validation of Proximity Operations and Formation Flying
In spacecraft Rendezvous, Proximity Operations (RPO), and Formation Flying (FF), the Guidance Navigation and Control (GNC) system is safety-critical and must meet strict performance requirements. However, validating such systems is challenging due to the complexity of the space environment, necessitating a verification and validation (V&V) process that bridges simulation and real-world behavior. The key contribution of this paper is a unified, end-to-end digital and robotic twinning framework t…

Mind the Gap: Conformative Decoding to Improve Output Diversity of Instruction-Tuned Large Language Models
Max Peeperkorn, Tom Kouwenhoven, Dan Brown, Anna Jordanous
https://arxiv.org/abs/2507.20956

Mind the Gap: Conformative Decoding to Improve Output Diversity of Instruction-Tuned Large Language Models
Instruction-tuning large language models (LLMs) reduces the diversity of their outputs, which has implications for many tasks, particularly for creative tasks. This paper investigates the ``diversity gap'' for a writing prompt narrative generation task. This gap emerges as measured by current diversity metrics for various open-weight and open-source LLMs. The results show significant decreases in diversity due to instruction-tuning. We explore the diversity loss at each fine-tuning stage for th…

The Impact of Fine-tuning Large Language Models on Automated Program Repair
Roman Mach\'a\v{c}ek, Anastasiia Grishina, Max Hort, Leon Moonen
https://arxiv.org/abs/2507.19909

The Impact of Fine-tuning Large Language Models on Automated Program Repair
Automated Program Repair (APR) uses various tools and techniques to help developers achieve functional and error-free code faster. In recent years, Large Language Models (LLMs) have gained popularity as components in APR tool chains because of their performance and flexibility. However, training such models requires a significant amount of resources. Fine-tuning techniques have been developed to adapt pre-trained LLMs to specific tasks, such as APR, and enhance their performance at far lower co…

MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning
Yong-Cheng Liaw, Shuo-Han Chen
https://arxiv.org/abs/2505.23254 https://

MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning
Owing to the huge success of generative artificial intelligence (AI), large language models (LLMs) have emerged as a core subclass, underpinning applications such as question answering, text generation, and code completion. While fine-tuning these models on domain-specific data can yield significant performance gains, it also poses daunting computational challenges, especially for researchers and small organizations with limited hardware resources. Although SSD offloading (i.e., ZeRO-Infinity) …

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
Xiaoqing Liu, Kehan Zeng, Zedong Luo, Yangshuai Wang, Teng Zhao, Zhenli Xu
https://arxiv.org/abs/2506.21935

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
Universal machine-learned interatomic potentials (U-MLIPs) have demonstrated broad applicability across diverse atomistic systems but often require fine-tuning to achieve task-specific accuracy. While the number of available U-MLIPs and their fine-tuning applications is rapidly expanding, there remains a lack of systematic guidance on how to effectively fine-tune these models. This tutorial provides a comprehensive, step-by-step guide to fine-tuning U-MLIPs for computational materials modeling.…

How many pixels are there in a polarized pulsar timing array map?
Dylan L. Jow, Anna Tsai, Ue-Li Pen
https://arxiv.org/abs/2507.21380 https://arxiv.org/pdf…

How many pixels are there in a polarized pulsar timing array map?
The standard approach to searching for gravitational wave signatures in pulsar timing array (PTA) data has been to compare the theoretical Hellings and Downs (HD) curve with the observed correlations in pulsar timing residuals as a function of angular separation on the sky between pulsar pairs. While this approach has successfully produced evidence for the presence of nanohertz-wavelength gravitational waves, it does not, on its own, produce any directional information. It is also insensitive t…

This https://arxiv.org/abs/2412.06481 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

DeePC-Hunt: Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization
This paper introduces Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization (DeePC-Hunt), a backpropagation-based method for automatic hyperparameter tuning of the DeePC algorithm. The necessity for such a method arises from the importance of hyperparameter selection to achieve satisfactory closed-loop DeePC performance. The standard methods for hyperparameter selection are to either optimize the open-loop performance, or use manual guess-and-check. Optimizing th…

Higher-order Tuning of Interface Physics in Multiphase Lattice Boltzmann
Matteo Lulli, Emily S. C. Ching
https://arxiv.org/abs/2505.23647 https://

Higher-order Tuning of Interface Physics in Multiphase Lattice Boltzmann
Tuning the interface properties of multiphase models is of paramount importance to the final goal of achieving a one-to-one matching with nucleation and cavitation experiments. The surface tension, at the leading order, and the Tolman length, at higher order, play a crucial role in the estimation of the free-energy barrier determining the experimentally observed nucleation rates. The lattice Boltzmann method allows for a computationally efficient modelling approach of multiphase flows, however,…

This https://arxiv.org/abs/2412.06481 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

DeePC-Hunt: Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization
This paper introduces Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization (DeePC-Hunt), a backpropagation-based method for automatic hyperparameter tuning of the DeePC algorithm. The necessity for such a method arises from the importance of hyperparameter selection to achieve satisfactory closed-loop DeePC performance. The standard methods for hyperparameter selection are to either optimize the open-loop performance, or use manual guess-and-check. Optimizing th…