arXiv subjects

Daniel Seaton

Publications and source records attributed to Daniel Seaton.

5 recordsLinked to original sources

arXiv2025

Supra-arcade downflows in an extensive fan associated with a giant quiescent solar filament eruption

We investigate the aftermath of a giant quiescent solar filament eruption on December 24, 2023. One feature of the eruption is an extensive fan above the filament channel that is about three times as wide as similar structures that appear above active regions (ARs) during solar flares. The fan contains numerous supra-arcade downflows (SADs), and we investigate the largest SADs with continuous Hinode X-ray Telescope (XRT) observations. The measured maximum width of the SADs in this event is at least three times the maximum width of SADs observed in AR flares, whereas the velocities of the largest SADs are similar to the typical values of AR SADs. The kinetic characteristics of the largest SADs observed in this event align with previous model predictions, where SADs originate from the non-linear development of Rayleigh-Taylor type instabilities. In this scenario, the larger system size allows the existence of larger-scale instabilities, while the development of the velocities of these instabilities is expected to be independent of the system size. Compared to AR flares, the temperature and emission measure in this event are lower, and there is less overall radiation, resulting in no evident Geostationary Operational Environmental Satellite (GOES) signature. Similar to those in AR flares, SADs show lower temperatures compared to the surrounding fan plasma. Our observations show that SADs are present in a wide variety of eruptions. The reconnection mechanisms present in quiescent filament eruptions are similar to those driving more compact eruptions originating from ARs.

astro-ph.SR↗

arXiv2024

Fine-tuning Protein Language Models with Deep Mutational Scanning improves Variant Effect Prediction

Protein Language Models (PLMs) have emerged as performant and scalable tools for predicting the functional impact and clinical significance of protein-coding variants, but they still lag experimental accuracy. Here, we present a novel fine-tuning approach to improve the performance of PLMs with experimental maps of variant effects from Deep Mutational Scanning (DMS) assays using a Normalised Log-odds Ratio (NLR) head. We find consistent improvements in a held-out protein test set, and on independent DMS and clinical variant annotation benchmarks from ProteinGym and ClinVar. These findings demonstrate that DMS is a promising source of sequence diversity and supervised training data for improving the performance of PLMs for variant effect prediction.

q-bio.GN↗

arXiv2023

Work-Life Balance Starts with Proper Deadlines and Exemplary Agencies

Diversity, equity and inclusion (DEI) programs can only be implemented successfully if proper work-life balance is possible in Heliophysics (and in STEM field in general). One of the core issues stems from the culture of "work-above-life" associated with mission concepts, development, and implementation but also the expectations that seem to originate from numerous announcements from NASA (and other agencies). The benefits of work-life balance are well documented; however, the entire system surrounding research in Heliophysics hinders or discourages proper work-life balance. For example, there does not seem to be attention paid by NASA Headquarters (HQ) on the timing of their announcements regarding how it will be perceived by researchers, and how the timing may promote a culture where work trumps personal life. The same is true for remarks by NASA HQ program officers during panels or informal discussions, where seemingly innocuous comments may give a perception that work is expected after "normal" work hours. In addition, we are calling for work-life balance plans and implementation to be one of the criteria used for down-selection and confirmation of missions (Key Decision Points: KDP-B, KDP-C).

astro-ph.IM↗

arXiv2021

SunCET: A compact EUV instrument to fill a critical observational gap

The Sun Coronal Ejection Tracker (SunCET) is an extreme ultraviolet imager and spectrograph instrument concept for tracking coronal mass ejections through the region where they experience the majority of their acceleration: the difficult-to-observe middle corona. It contains a wide field of view (0-4~\Rs) imager and a 1~Å spectral-resolution-irradiance spectrograph spanning 170-340~Å. It leverages new detector technology to read out different areas of the detector with different integration times, resulting in what we call "simultaneous high dynamic range", as opposed to the traditional high dynamic range camera technique of subsequent full-frame images that are then combined in post-processing. This allows us to image the bright solar disk with short integration time, the middle corona with a long integration time, and the spectra with their own, independent integration time. Thus, SunCET does not require the use of an opaque or filtered occulter. SunCET is also compact -- $\sim$15 $\times$ 15 $\times$ 10~cm in volume -- making it an ideal instrument for a CubeSat or a small, complementary addition to a larger mission. Indeed, SunCET is presently in a NASA-funded, competitive Phase A as a CubeSat and has also been proposed to NASA as an instrument onboard a 184 kg Mission of Opportunity.

astro-ph.SR↗

arXiv2017

Delving Deeper into MOOC Student Dropout Prediction

In order to obtain reliable accuracy estimates for automatic MOOC dropout predictors, it is important to train and test them in a manner consistent with how they will be used in practice. Yet most prior research on MOOC dropout prediction has measured test accuracy on the same course used for training the classifier, which can lead to overly optimistic accuracy estimates. In order to understand better how accuracy is affected by the training+testing regime, we compared the accuracy of a standard dropout prediction architecture (clickstream features + logistic regression) across 4 different training paradigms. Results suggest that (1) training and testing on the same course ("post-hoc") can overestimate accuracy by several percentage points; (2) dropout classifiers trained on proxy labels based on students' persistence are surprisingly competitive with post-hoc training (87.33% versus 90.20% AUC averaged over 8 weeks of 40 HarvardX MOOCs); and (3) classifier performance does not vary significantly with the academic discipline. Finally, we also research new dropout prediction architectures based on deep, fully-connected, feed-forward neural networks and find that (4) networks with as many as 5 hidden layers can statistically significantly increase test accuracy over that of logistic regression.

cs.AI↗