arXiv:2607. 27645v1 Announce Type: cross Abstract: Wavelength selection is one of the important preprocessing methods in near-infrared spectroscopy to improve prediction accuracy and interpretability of spectral data.
By Mitsunobu Kanebako, Ami S. Koshikawa, Masaru Hitomi, Takuro Tanaka, Mahito Chiba, Maiko Mori, Masayuki Ohzeki
arXiv:2605.00068v2 Announce Type: replace-cross
Abstract: Inertial Confinement Fusion (ICF) holds transformative promise for sustainable, near-limitless clean energy, yet remains constrained by prohi...
By Ricardo Luna Gutierrez, Sahand Ghorbanpour, Rahman Ejaz, Varchas Gopalaswamy, Riccardo Betti, Vineet Gundecha, Aarne Lees, Soumyendu Sarkar
arXiv:2606. 20087v1 Announce Type: new Abstract: Additive manufacturing process optimization requires precise parameter control to minimize defects such as porosity.
By Kianoush Aqabakee, Leonardo Stella
arXiv:2603. 23101v3 Announce Type: replace Abstract: Intelligent spectroscopy serves as a pivotal element in AI-driven closed-loop scientific discovery, functioning as the critical bridge between matter structure and artificial intelligence.
By Yutang Ge, Yaning Cui, Hanzheng Li, Jun-Jie Wang, Fanjie Xu, Jinhan Dong, Yongqi Jin, Dongxu Cui, Peng Jin, Guojiang Zhao, Hengxing Cai, Tianci Yangfeng, Xueqing Chen, Hongshuai Wang, Rong Zhu, Linfeng Zhang, Xiaohong Ji, Zhifeng Gao
arXiv:2607. 03140v1 Announce Type: new Abstract: Industrial automation increasingly demands control strategies that balance operational performance with strict energy efficiency requirements.
By Georg Sch\"afer, Jakob Rehrl, Stefan Huber, Simon Hirlaender
The article investigates how Bayesian optimization can improve the ACTS parameter optimization suite for charged‑particle reconstruction. By comparing Expected Improvement and Upper Confidence Bound with TPE and random search on an eight‑parameter problem, extending the best method to fifteen parameters, and applying Expected Hypervolume Improvement for multi‑objective tuning, the study shows that Bayesian acquisition methods find strong configurations earlier and maintain advantages in held‑out validation. The results demonstrate that Bayesian optimization enhances ACTS auto‑tuning through more efficient evaluations, broader search spaces, and the ability to select from non‑dominated trade‑off solutions.
By Chance LaVoie, Qi Bin Lei, Rocky Bala Garg, Lauren Tompkins