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We present photometric and spectroscopic observations and analysis of SN 2021bxu (ATLAS21dov), a low-luminosity, fast-evolving Type IIb supernova (SN). SN 2021bxu is unique, showing a large initial decline in brightness followed by a short plateau phase. With $M_r = -15.93 \pm 0.16\, \mathrm{mag}$ during the plateau, it is at the lower end of the luminosity distribution of stripped-envelope supernovae (SE-SNe) and shows a distinct ∼10 d plateau not caused by H- or He-recombination. SN 2021bxu shows line velocities which are at least $\sim 1500\, \mathrm{km\, s^{-1}}$ slower than typical SE-SNe. It is photometrically and spectroscopically similar to Type IIb SNe during the photospheric phases of evolution, with similarities to Ca-rich IIb SNe. We find that the bolometric light curve is best described by a composite model of shock interaction between the ejecta and an envelope of extended material, combined with a typical SN IIb powered by the radioactive decay of 56Ni. The best-fitting parameters for SN 2021bxu include a 56Ni mass of $M_{\mathrm{Ni}} = 0.029^{+0.004}_{-0.005}\, \mathrm{{\rm M}_{\odot }}$, an ejecta mass of $M_{\mathrm{ej}} = 0.61^{+0.06}_{-0.05}\, \mathrm{{\rm M}_{\odot }}$, and an ejecta kinetic energy of $K_{\mathrm{ej}} = 8.8^{+1.1}_{-1.0} \times 10^{49}\, \mathrm{erg}$. From the fits to the properties of the extended material of Ca-rich IIb SNe we find a trend of decreasing envelope radius with increasing envelope mass. SN 2021bxu has MNi on the low end compared to SE-SNe and Ca-rich SNe in the literature, demonstrating that SN 2021bxu-like events are rare explosions in extreme areas of parameter space. The progenitor of SN 2021bxu is likely a low-mass He star with an extended envelope.

 

We present new 0.3–21μm photometry of SN 2021aefx in the spiral galaxy NGC 1566 at +357 days afterB-band maximum, including the first detection of any Type Ia supernova (SN Ia) at >15μm. These observations follow earlier JWST observations of SN 2021aefx at +255 days after the time of maximum brightness, allowing us to probe the temporal evolution of the emission properties. We measure the fraction of flux emerging at different wavelengths and its temporal evolution. Additionally, the integrated 0.3–14μm decay rate of Δm_0.3–14= 1.35 ± 0.05 mag/100 days is higher than the decline rate from the radioactive decay of⁵⁶Co of ∼1.2 mag/100 days. The most plausible explanation for this discrepancy is that flux is shifting to >14μm, and future JWST observations of SNe Ia will be able to directly test this hypothesis. However, models predicting nonradiative energy loss cannot be excluded with the present data.

 

We present optical, infrared, ultraviolet, and radio observations of SN 2022xkq, an underluminous fast-declining Type Ia supernova (SN Ia) in NGC 1784 (D≈ 31 Mpc), from <1 to 180 days after explosion. The high-cadence observations of SN 2022xkq, a photometrically transitional and spectroscopically 91bg-like SN Ia, cover the first days and weeks following explosion, which are critical to distinguishing between explosion scenarios. The early light curve of SN 2022xkq has a red early color and exhibits a flux excess that is more prominent in redder bands; this is the first time such a feature has been seen in a transitional/91bg-like SN Ia. We also present 92 optical and 19 near-infrared (NIR) spectra, beginning 0.4 days after explosion in the optical and 2.6 days after explosion in the NIR. SN 2022xkq exhibits a long-lived Ci1.0693μm feature that persists until 5 days post-maximum. We also detect Ciiλ6580 in the pre-maximum optical spectra. These lines are evidence for unburnt carbon that is difficult to reconcile with the double detonation of a sub-Chandrasekhar mass white dwarf. No existing explosion model can fully explain the photometric and spectroscopic data set of SN 2022xkq, but the considerable breadth of the observations is ideal for furthering our understanding of the processes that produce faint SNe Ia.

 

Necrotizing enterocolitis (NEC) is a devastating gastrointestinal disease of incompletely understood pathophysiology predominantly affecting premature infants. While NEC is associated with microbial invasion of intestinal tissues, and mucus modulates interactions between microbes and underlying tissues, variations in mucus barrier properties with NEC-associated risk factors have not been investigated. This study explored differences in mucus composition (total protein, DNA, mucin content, sialic acid, and immunoregulatory proteins), as well as structural and transport properties, assessed by tracking of particles and bacteria (E. coliandE. cloacae) with developmental age and exposure to NEC stressors in Sprague Dawley rats. Early developmental age (5 day old) was characterized by a more permeable mucus layer relative to 21 day old pups, suggesting immaturity may contribute to exposure of the epithelium to microbes. Exposure to NEC stressors was associated with reduced mucus permeability, which may aid in survival. Feeding with breastmilk as opposed to formula reduces incidence of NEC. Thus, NEC-stressed (N-S) rat pups were orally dosed with breastmilk components lysozyme (N-S-LYS) or docosahexaenoic acid (N-S-DHA). N-S-LYS and N-S-DHA pups had a less permeable mucus barrier relative to N-S pups, which suggests the potential of these factors to strengthen the mucus barrier and thus protect against disease.

 

In cognitive diagnostic assessment, multiple fine-grained attributes are measured simultaneously. Attribute hierarchies are considered important structural features of cognitive diagnostic models (CDMs) that provide useful information about the nature of attributes. Templin and Bradshaw first introduced a hierarchical diagnostic classification model (HDCM) that directly takes into account attribute hierarchies, and hence, HDCM is nested within more general CDMs. They also formulated an empirically driven hypothesis test to statistically test one hypothesized link (between two attributes) at a time. However, their likelihood ratio test statistic does not have a known reference distribution, so it is cumbersome to perform hypothesis testing at scale. In this article, we studied two exploratory approaches that could learn the attribute hierarchies directly from data, namely, the latent variable selection (LVS) approach and the regularized latent class modeling (RLCM) approach. An identification constraint was proposed for the LVS approach. Simulation results revealed that both approaches could successfully identify different types of attribute hierarchies, when the underlying CDM is either the deterministic input noisy and gate model or the saturated log-linear CDM. The LVS approach outperformed the RLCM approach, especially when the total number of attributes increases.

 

Item nonresponses are prevalent in standardized testing. They happen either when students fail to reach the end of a test due to a time limit or quitting, or when students choose to omit some items strategically. Oftentimes, item nonresponses are nonrandom, and hence, the missing data mechanism needs to be properly modeled. In this paper, we proposed to use an innovative item response time model as a cohesive missing data model to account for the two most common item nonresponses: not‐reached items and omitted items. In particular, the new model builds on a behavior process interpretation: a person chooses to skip an item if the required effort exceeds the implicit time the person allocates to the item (Lee & Ying, 2015; Wolf, Smith, & Birnbaum, 1995), whereas a person fails to reach the end of the test due to lack of time. This assumption was verified by analyzing the 2015 PISA computer‐based mathematics data. Simulation studies were conducted to further evaluate the performance of the proposed Bayesian estimation algorithm for the new model and to compare the new model with a recently proposed “speed‐accuracy + omission” model (Ulitzsch, von Davier, & Pohl, 2019). Results revealed that all model parameters could recover properly, and inadequately accounting for missing data caused biased item and person parameter estimates.

 

Software Managed Manycore (SMM) architectures have been proposed as a solution for scaling the memory architecture. In a typical SMM architecture, Scratch Pad Memories (SPM) is used instead of caches, and data must be explicitly managed in software. While all code and data need to be managed, heap management on SMMs is especially challenging due to the highly dynamic nature of heap data access. Existing techniques spend over 90% of execution time on heap data management, which largely compromised the power efficiency of SMM architectures. This paper presents compiler-based efficient techniques that reduce heap management overhead. Experimental results on benchmarks from MiBench executing on an SMM processor modeled in Gem5 demonstrate that our approach implemented in LLVM 3.8 can improve execution time by an average of 80%, compared to the state-of-the-art.