Papers where the original authors have engaged with our plain-language explanation.
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.
1343 papers reviewed by authors · 581–590 / 1343
Amidst the global growth of quantum science, this report leverages a worldwide survey to demonstrate that marginalized scientists face disproportionate hardships, arguing that adopting an intersectional, feminist approach to equity, diversity, and inclusion is essential for retaining talent and ensuring the resilience of the quantum ecosystem.
This study demonstrates that operant conditioning significantly shapes the sociability of Indian free-ranging dogs, where positive cues enhance approach behaviors that partially generalize to unfamiliar humans, while threatening cues induce caution and increased latency without similarly generalizing to reduce overall approach.
This paper demonstrates that hyperons in core-collapse supernovae reach local chemical equilibrium rapidly via nonleptonic reactions and significantly enhance muon (anti)neutrino opacities through semileptonic channels, thereby influencing proto-neutron star evolution and flavor-dependent neutrino transport.
This paper investigates the tunneling-induced hybridization and coherent oscillations of Jackiw-Rebbi zero modes in a modified Su-Schrieffer-Heeger chain, demonstrating how finite overlap between bound states at Dirac-type gap closing points enables controllable topological quantum-state transfer.
OmniPilot is an uncertainty-aware inference advisor for heterogeneous GPU clusters that predicts serving costs and abstains from unreliable configurations using conformally calibrated quantile models and out-of-distribution detection, thereby optimizing economic utility with high accuracy across diverse hardware and precision settings.
This study demonstrates that while LLM personas can reveal some stable personality-driven patterns in visualization design tasks like color assignment and chart choice, their outputs are heavily dependent on model configuration and task context, positioning them as useful exploratory probes rather than reliable substitutes for human participants.
This study extends modified wavenumber analysis to Physics-Informed Neural Networks (PINNs) to systematically evaluate their spectral accuracy on high-frequency wave problems, revealing that deeper architectures, specific activation functions like sinusoidal or tanh-Gaussian, and an optimal number of Fourier modes () are critical for minimizing dispersion and dissipation errors.
This study presents a three-phase deep learning framework that combines LSTM-based pH prediction with Deep Reinforcement Learning control, validated against African soil profiles using synthetic data, to provide a scalable and transferable solution for optimizing mine reclamation in data-scarce Sub-Saharan regions.
This study presents an interpretable forecasting framework using penalized logistic regression and bootstrap stability selection on historical team data to predict FIFA World Cup progression, identifying key predictors like market value and FIFA ranking while forecasting Argentina, France, Spain, England, Germany, and the Netherlands as top contenders for the 2026 tournament.
This mixed-method study demonstrates that brand communication, when moderated by CSR and cultural lifestyles, serves as a strategic tool for building reputable brands, enhancing consumer preferences, and translating into brand equity.