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Sarah C. Steele

Publications and source records attributed to Sarah C. Steele.

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Mercury's Crustal Magnetization Indicates a Stronger Ancient Dynamo

Mercury is the only terrestrial planet in the solar system other than Earth with an active dynamo magnetic field (~200 nT at the equatorial surface). Furthermore, Mercury's ~3.9-3.7-billion-year-old (Ga) crust is strongly magnetized (~10 nT at ~30-km altitude), indicating the presence of a past dynamo. However, the strength of the past dynamo field and the mechanism that generated it are unknown. To address this, we performed three-dimensional magnetohydrodynamic simulations of the ancient solar wind interaction with the planetary field coupled with crustal thermal evolution and magnetization models. We show that the crustal magnetization was likely produced by a dipole field with equatorial surface strength of at least ~2,000 nT and possibly as high as ~30,000 nT for a dynamo with a reversal frequency greater than once per million years. Such strong fields likely exclude both the solar wind feedback and thermoelectric dynamo mechanisms at 3.7 Ga ago. Instead, our results are compatible with the past dynamo being generated by a nearly fully convective core.

astro-ph.EP

Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals

Currently there is no validated objective measure of pain. Recent neuroimaging studies have explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to measure alterations in brain function in evoked and ongoing pain. In this study, we applied multi-task machine learning methods to derive a practical algorithm for pain detection derived from fNIRS signals in healthy volunteers exposed to a painful stimulus. Especially, we employed multi-task multiple kernel learning to account for the inter-subject variability in pain response. Our results support the use of fNIRS and machine learning techniques in developing objective pain detection, and also highlight the importance of adopting personalized analysis in the process.

cs.LG