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We discuss ASASSN-24fw, a 13th-magnitude star that optically faded by $\Delta g=4.12\pm 0.02$ mag starting in September 2024 after over a decade of quiescence in ASAS-SN. The dimmimg lasted $$8 months before returning to quiescence in late May 2025. The spectral energy distribution (SED) before the event is that of a pre-main sequence or a modestly evolved F star with some warm dust emission. The shape of the optical SED during the dim phase is unchanged and the optical and near-infrared spectra are those of an F star. The SED and the dilution of some of the F star infrared absorption features near minimum suggest the presence of a $$ $0.25$ M_$$ M dwarf binary companion. The 43.8 year period proposed by Nair & Denisenko (2024) appears correct and is probably half the precession period of a circumbinary disk. The optical eclipse is nearly achromatic, although slightly deeper in bluer filters, $\Delta (g-z)=0.31\pm 0.15$ mag, and the $V$ band emission is polarized by up to 4%. The materials most able to produce such small optical color changes and a high polarization are big ($$20 $\mu$ m) carbonaceous or water ice grains. Particle distributions dominated by big grains are seen in protoplanetary disks, Saturn-like ring systems and evolved debris disks. We also carry out a survey of occultation events, finding 46 additional systems, of which only 7 (4) closely match $\epsilon$ Aurigae (KH 15D), the two archetypes of stars with long and deep eclipses. The full sample is widely distributed in an optical color-magnitude diagram, but roughly half show a mid-IR excess. It is likely many of the others have cooler dust since it seems essential to produce the events. 

This Letter extends a recent IceCube sterile neutrino search to include unstable sterile neutrinos within the context of a model termed $3+1+\mathrm{Decay}$ , which expands upon the $3+1$ model by introducing sterile neutrino decay to invisible particles with coupling constant $g^2$ . The model is attractive since it reduces tension between oscillation experiments within the global fits and with constraints that come from cosmological observables. The analysis uses 10.7 years of up-going muon neutrino data with energy 500 GeV to 100 TeV and with improved reconstruction and modeling of systematics. The best-fit point is found to be $g^2=0$ , ${\sin }^2\left(2{\theta }_{24}\right)=0.16$ , and $\mathrm{\Delta }{m}_{41}^2=3.5{\mathrm{eV}}^2$ , in agreement with the recent $3+1$ sterile neutrino search. Values of $g^2\ge \pi$ are excluded at 95% confidence level. This result substantially limits decay parameter space indicated by recent global fits, disfavoring the decay scenario. 

Radio pulses generated by cosmic-ray air showers can be used to reconstruct key properties like the energy and depth of the electromagnetic component of cosmic-ray air showers. Radio detection threshold, influenced by natural and anthropogenic radio background, can be reduced through various techniques. In this work, we demonstrate that convolutional neural networks (CNNs) are an effective way to lower the threshold. We developed two CNNs: a classifier to distinguish radio signal waveforms from background noise and a denoiser to clean contaminated radio signals. Following the training and testing phases, we applied the networks to air-shower data triggered by scintillation detectors of the prototype station for the enhancement of IceTop, IceCube’s surface array at the South Pole. Over a four-month period, we identified 554 cosmic-ray events in coincidence with IceTop, approximately five times more compared to a reference method based on a cut on the signal-to-noise ratio. Comparisons with IceTop measurements of the same air showers confirmed that the CNNs reliably identified cosmic-ray radio pulses and outperformed the reference method. Additionally, we find that CNNs reduce the false-positive rate of air-shower candidates and effectively denoise radio waveforms, thereby improving the accuracy of the power and arrival time reconstruction of radio pulses. 

Ascomycota, the most speciose phylum of fungi, is a complex entity, comprising three diversesubphyla: Pezizomycotina, Saccharomycotina, and Taphrinomycotina. The largest and most diversesubphylum, Pezizomycotina, is a rich tapestry of 16 classes and 171 orders. Saccharomycotina, thesecond largest subphylum, is a diverse collection of seven classes and 12 orders, whileTaphrinomycotina, the smallest, is a unique assembly of six classes and six orders. Over the pastdecade, numerous taxonomic studies have focused on the generic, family, and class classifications ofAscomycota. These efforts, well-documented across various databases, are crucial for acomprehensive understanding of the classification. However, the study of taxonomy at the ordinallevel, a crucial tier in the taxonomic hierarchy, has been largely overlooked. In a global collaborationwith mycologists and lichenologists, this study presents the first comprehensive information on theorders within Pezizomycotina and Taphrinomycotina. The recent taxonomic classification ofSaccharomycotina has led to the exclusion of this subphylum from the present study, as an immediaterevision is not necessary. Each order is thoroughly discussed, highlighting its historical significance,current status, key identification characteristics, evolutionary relationships, ecological and economicroles, future recommendations, and updated family-level classification. Teaching diagrams for thelife cycles of several orders, viz. Asterinales, Helotiales, Hypocreales, Laboulbeniales, Meliolales,Mycosphaerellales, Ophiostomatales, Pezizales, Pleosporales, Phyllachorales, Rhytismatales,Sordariales, Venturiales, Xylariales (Pezizomycotina) and Pneumocystidales,Schizosaccharomycetales and Taphrinales (Taphrinomycotina) are provided. Each diagram is explained with a representative genus/genera of their sexual and asexual cycles of each order. WithinPezizomycotina, Dothideomycetes contains the highest number of orders, with 57, followed bySordariomycetes (52 orders), Lecanoromycetes (21 orders), Eurotiomycetes and Leotiomycetes (12orders each), Laboulbeniomycetes (3 orders), and Arthoniomycetes and Xylonomycetes (2 orderseach). Candelariomycetes, Coniocybomycetes, Geoglossomycetes, Lichinomycetes, Orbiliomycetes,Pezizomycetes, Sareomycetes, and Xylobotryomycetes each contain a single order, whileThelocarpales and Vezdaeales are treated as incertae sedis within Pezizomycotina. Notably, theclasses Candelariomycetes, Coniocybomycetes, Geoglossomycetes, Sareomycetes, andXylonomycetes, all recently grouped under Lichinomycetes, are treated as separate classes based onphylogenetic analysis and current literature. Within Lecanoromycetes, the synonymization ofSporastatiales with Rhizocarpales and Sarrameanales with Schaereriales is not supported in thephylogenetic analysis. These orders are retained separately, and the justifications are provided undereach section as well as in the discussion. Within Leotiomycetes, the order Medeolariales, which wasonce considered part of Helotiales, is treated as a distinct order based on phylogenetic evidence. Theclassification of Medeolariales may change as more data becomes available from different generegions. Lahmiales (Leotiomycetes) is not included in the phylogenetic analysis due to a lack ofmolecular data. Sareomycetes and Xylonomycetes are treated as separate classes. Spathulosporamixed with Lulworthiales and the inclusion of Spathulosporales within Lulworthiomycetidae issupported and extant molecular sampling is important to resolve the phylogenetic boundaries ofmembers of this subclass. The majority of the classes of Pezizomycotina and Taphrinomycotinaformed monophyletic clades in the phylogenetic analysis conducted based on SSU, LSU, 5.8S, TEFand RPB2 sequence data. However, Arthoniomycetes nested with the basal lineage ofDothideomycetes and formed a monophyletic clade also known as the superclass, Dothideomyceta.In Taphrinomycotina, a single order is accepted within each class. 

Traditional accelerated life test plans are typically based on optimizing the C-optimality for minimizing the variance of an interested quantile of the lifetime distribution. These methods often rely on some specified planning values for the model parameters, which are usually unknown prior to the actual tests. The ambiguity of the specified parameters can lead to suboptimal designs for optimizing the reliability performance of interest. In this paper, we propose a sequential design strategy for life test plans based on considering dual objectives. In the early stage of the sequential experiment, we suggest allocating more design locations based on optimizing the D-optimality to quickly gain precision in the estimated model parameters. In the later stage of the experiment, we can allocate more observations based on optimizing the C-optimality to maximize the precision of the estimated quantile of the lifetime distribution. We compare the proposed sequential design strategy with existing test plans considering only a single criterion and illustrate the new method with an example on the fatigue testing of polymer composites. 

Incomplete or inconsistent temporal neuroimaging records of patients over time pose a major challenge to accurately predict clinical scores for diagnosing Alzheimer’s Disease (AD). In this paper, we present an unsupervised method to learn enriched imaging biomarker representations that can simultaneously capture the information conveyed by all the baseline neuroimaging measures and the progressive variations of the available follow-up measurements of every participant. Our experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show improved performance in predicting cognitive outcomes thereby demonstrating the effectiveness of our proposed method.