For every paper on this page, at least one of the original authors has seen our plain-language explanation and engaged with it — either confirming it reads accurately or requesting corrections that we then applied. An endorsement does not mean the authors formally approve every sentence, but it does mean the explanation has passed the eyes of the people who wrote the paper.

990 papers reviewed by authors · 811–820 / 990

🔬 materials science

Ultrafast Energy Absorption in Silicon Controlled by Two-Color Double Pulses

This theoretical study demonstrates that ultrafast energy absorption in crystalline silicon can be precisely controlled by two-color femtosecond double pulses, where the optimal wavelength combination and underlying excitation mechanisms shift from multiphoton interband absorption to tunneling ionization and intraband acceleration depending on the laser intensity regime.

Eiyu S. Gushiken, Mizuki Tani, Hiroki Katow, Kenichi L. Ishikawa2026-04-29✓ Author reviewed
🔭 astrophysics

pyTANSPEC v1.0 and HxRGproc: Updated packages to Clean and Reduce TANSPEC data

This paper introduces upgraded versions of the `pyTANSPEC` and `HxRGproc` Python packages, which provide enhanced data reduction capabilities for the TANSPEC instrument, including support for all slit widths, improved wavelength calibration, flux calibration, and automated cleaning of detector readout frames.

Varghese Reji, Joe P. Ninan, Supriyo Ghosh, Devendra K. Ojha, Saurabh Sharma2026-04-28✓ Author reviewed
🌀 nonlinear sciences

Non-Floquet oscillations of a parametrically driven rigid planar pendulum

This paper identifies a novel type of nonlinear oscillation in a parametrically driven rigid planar pendulum that occurs in regions predicted to be stable by Floquet analysis, characterized by periods longer than twice the driving period and a unique power spectrum where the two dominant response frequencies sum to the driving frequency.

Rebeka Sarkar, Krishna Kumar, Sugata Pratik Khastgir2026-04-27✓ Author reviewed
📊 statistics

Is K-fold cross validation the best model selection method for Machine Learning?

This paper proposes a novel K-fold CUBV statistical test that combines K-fold cross-validation with Bayesian upper bounds and concentration inequalities to provide a robust criterion for validating machine learning accuracy and detecting effects while avoiding excess false positives, particularly in scenarios with small sample sizes and heterogeneous data.

Juan M Gorriz, R. Martin Clemente, F Segovia, J Ramirez, A Ortiz, J. Suckling2026-04-24✓ Author reviewed