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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. 

Abstract We present ultraviolet/optical/near-infrared observations and modeling of Type II supernovae (SNe II) whose early time (δt< 2 days) spectra show transient, narrow emission lines from shock ionization of confined (r< 10¹⁵cm) circumstellar material (CSM). The observed electron-scattering broadened line profiles (i.e., IIn-like) of Hi, Hei/ii, Civ, and Niii/iv/vfrom the CSM persist on a characteristic timescale (t_IIn) that marks a transition to a lower-density CSM and the emergence of Doppler-broadened features from the fast-moving SN ejecta. Our sample, the largest to date, consists of 39 SNe with early time IIn-like features in addition to 35 “comparison” SNe with no evidence of early time IIn-like features, all with ultraviolet observations. The total sample includes 50 unpublished objects with a total of 474 previously unpublished spectra and 50 multiband light curves, collected primarily through the Young Supernova Experiment and Global Supernova Project collaborations. For all sample objects, we find a significant correlation between peak ultraviolet brightness and botht_IInand the rise time, as well as evidence for enhanced peak luminosities in SNe II with IIn-like features. We quantify mass-loss rates and CSM density for the sample through the matching of peak multiband absolute magnitudes, rise times,t_IIn, and optical SN spectra with a grid of radiation hydrodynamics and non-local thermodynamic equilibrium radiative-transfer simulations. For our grid of models, all with the same underlying explosion, there is a trend between the duration of the electron-scattering broadened line profiles and inferred mass-loss rate: $t_{\mathrm{IIn}}\approx 3.8[\dot{M}/$(0.01M_⊙yr⁻¹)] days. 

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.