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Abstract Accurate estimates of geothermal heat flow (GHF) are critical for predicting basal melting and identifying stable sites for ancient ice, yet GHF remains one of the least constrained boundary conditions for the Antarctic Ice Sheet. We evaluate nine published Antarctic GHF models against radar‐derived specularity content in the South Pole Basin (SPB), a geologically complex region in central East Antarctica. We also simulate an ensemble of synthetic GHF fields via a three‐parameter Markov Chain Monte Carlo algorithm to constrain the spatial variability required to match observed bed conditions. No existing GHF map captures the observed gradient in basal conditions significantly better than a uniform GHF field. Instead, the radar observations require a spatial GHF gradient aligned with a major ice‐sheet and geomorphological boundary within the SPB. Constraining basal thermal state in this region will require methods sensitive to shallow crustal heterogeneity and integration of radar‐based indicators directly into model frameworks. 

Abstract We present analysis of the plateau and late-time phase properties of a sample of 39 Type II supernovae (SNe II) that show narrow, transient, high-ionization emission lines (i.e., “IIn-like”) in their early-time spectra from interaction with confined, dense circumstellar material (CSM). Originally presented by W. V. Jacobson-Galán et al., this sample also includes multicolor light curves and spectra extending to late-time phases of 35 SNe with no evidence for IIn-like features at <2 days after first light. We measure photospheric phase light-curve properties for the distance-corrected sample and find that SNe II with IIn-like features have significantly higher luminosities and decline rates at +50 days than the comparison sample, which could be connected to inflated progenitor radii, lower ejecta mass, and/or persistent CSM interaction. However, we find no statistical evidence that the measured plateau durations and⁵⁶Ni masses of SNe II with and without IIn-like features arise from different distributions. We estimate progenitor zero-age main-sequence (ZAMS) masses for all SNe with nebular spectroscopy through spectral model comparisons and find that most objects, both with and without IIn-like features, are consistent with progenitor masses ≤12.5M_⊙. Combining progenitor ZAMS masses with CSM densities inferred from early-time spectra suggests multiple channels for enhanced mass loss in the final years before core collapse, such as a convection-driven chromosphere or binary interaction. Finally, we find spectroscopic evidence for ongoing ejecta-CSM interaction at radii >10¹⁶cm, consistent with substantial progenitor mass-loss rates of ∼10⁻⁴–10⁻⁵M_⊙yr⁻¹(v_w < 50 km s⁻¹) in the final centuries to millennia before explosion. 

We present the photometric and spectroscopic analysis of five Type Ibn supernovae (SNe): SN 2020nxt, SN 2020taz, SN 2021bbv, SN 2023utc, and SN 2024aej. These events share key observational features and belong to a family of objects similar to the prototypical Type Ibn SN 2006jc. The SNe exhibit rise times of approximately 10 days and peak absolute magnitudes ranging from −16.5 to −19 mag. Notably, SN 2023utc is the faintest Type Ibn SN discovered to date, with an exceptionally lowr-band absolute magnitude of −16.4 mag. The pseudo-bolometric light curves peak at (1 − 10)×10⁴²erg s⁻¹, with total radiated energies on the order of (1 − 10)×10⁴⁸erg. Spectroscopically, these SNe display a relatively slow spectral evolution. The early spectra are characterised by a hot blue continuum and prominent He Iemission lines. The early spectra also show blackbody temperatures exceeding 10 000 K, with a subsequent decline in temperature during later phases. Narrow He Ilines, which are indicative of unshocked circumstellar material (CSM), show velocities of approximately 1000 km s⁻¹. The spectra suggest that the progenitors of these SNe underwent significant mass loss prior to the explosion, resulting in a He-rich CSM. Our light curve modelling yielded estimates for the ejecta mass (M_ej) in the range 1 − 3 M_⊙with kinetic energies (E_Kin) of (0.1 − 1)×10⁵⁰erg. The inferred CSM mass ranges from 0.2 to 1 M_⊙. These findings are consistent with expectations for core collapse events arising from relatively massive envelope-stripped progenitors. 

Machine learning is being increasingly used by individuals, research institutions, and corporations. This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and resources to learn the model, and (b) a user-friendly query interface to access the model. However, such MLaaS systems raise privacy concerns such as model extraction. In model extraction attacks, adversaries mali- ciously exploit the query interface to steal the model. More precisely, in a model extraction attack, a good approximation of a sensitive or propri- etary model held by the server is extracted (i.e. learned) by a dishonest user who interacts with the server only via the query interface. This attack was introduced by Tramèr et al. at the 2016 USENIX Security Symposium, where practical attacks for various models were shown. We believe that better understanding the efficacy of model extraction attacks is paramount to designing secure MLaaS systems. To that end, we take the first step by (a) formalizing model extraction and discussing possible defense strategies, and (b) drawing parallels between model extraction and established area of active learning. In particular, we show that re- cent advancements in the active learning domain can be used to imple- ment powerful model extraction attacks, and investigate possible defense strategies. 

Machine learning is being increasingly used by individu- als, research institutions, and corporations. This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and resources to learn the model, and (b) a user-friendly query interface to access the model. However, such MLaaS systems raise concerns such as model extraction. In model extraction attacks, adversaries maliciously exploit the query interface to steal the model. More precisely, in a model extraction attack, a good approxi- mation of a sensitive or proprietary model held by the server is extracted (i.e. learned) by a dishonest user who interacts with the server only via the query interface. This attack was introduced by Tramèr et al. at the 2016 USENIX Security Symposium, where practical attacks for various models were shown. We believe that better understanding the efficacy of model extraction attacks is paramount to designing secure MLaaS systems. To that end, we take the first step by (a) formalizing model extraction and discussing possible defense strategies, and (b) drawing parallels between model extraction and established area of active learning. In particular, we show that recent advancements in the active learning domain can be used to implement powerful model extraction attacks, and investigate possible defense strategies.