Fine-tuned geospatial foundation models (GeoFMs) pretrained on large satellite archives have been shown to improve crop classification accuracy and geographic transferability. However, their operational performance beyond the training distribution remains poorly characterized. We evaluated the out-of-distribution perfo...
V. Kolluru, Rajat C. Shinde, Abdelhak Marouane et al.· 0 citations
The minimal sampling overhead of the quadratic collective phase $e^{-i\gamma K^2}$ over one layer of single-qubit rotations is decided by the continued fraction of $\gamma/\pi$. Here $K$ is a collective observable with spectrum $\{0,\dots,n\}$, such as the permutation-symmetric Hamming weight. This gate is the cardinal...
Zhi-Hui Wang, Sujit Roy, M. Maskey et al.· 0 citations
A quantum kernel fusing amplitude encoding of l2-normalized delay windows with a grouped single-qubit rotation layer that assigns contiguous temporal blocks to each qubit, making the circuit delay-window-aware and the first mechanistic localisation of quantum-kernel advantage to a specific dynamical regime of a classic...
Zhi-Hui Wang, Sujit Roy, Ata Akbari et al.· 0 citations
Lunar orbital missions, such as Lunar Reconnaissance Orbiter, Kaguya/SELENE, Gravity Recovery and Interior Laboratory, and Lunar Prospector, among others, provide rich multi-instrument observations, but their heterogeneity in sampling, projection, and conventions limits reproducible machine learning (ML). We introduce...
Himanshu Patil, Gabrielle Nyirjesy, R. Slank et al.· 0 citations
Among adaptation strategies, LoRA matches or surpasses full fine-tuning on crater detection and IMP segmentation while using far fewer trainable parameters, whereas full fine-tuning performs best for ice prospectivity regression.
Paolo Fraccaro, Gabrielle Nyirjesy, Daniela Szwarcman et al.· 0 citations
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