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Blind inpainting algorithms based on deep learning architectures have shown a remarkable performance in recent years, typically outperforming model-based methods both in terms of image quality and run time. However, neural network strategies typically lack a theoretical explanation, which contrasts with the well-understood theory underlying model-based methods. In this work, we leverage the advantages of both approaches by integrating theoretically founded concepts from transform domain methods and sparse approximations into a CNN-based approach for blind image inpainting. To this end, we present a novel strategy to learn convolutional kernels that applies a specifically designed filter dictionary whose elements are linearly combined with trainable weights. Numerical experiments demonstrate the competitiveness of this approach. Our results show not only an improved inpainting quality compared to conventional CNNs but also significantly faster network convergence within a lightweight network design. Our code is available at https://github.com/cv-stuttgart/SDPF_Blind-Inpainting. 

Abstract Fungi are one of the most diverse groups of organisms with an estimated number of species in the range of 2–3 million. The higher-level ranking of fungi has been discussed in the framework of molecular phylogenetics since Hibbett et al., and the definition and the higher ranks (e.g., phyla) of the ‘true fungi’ have been revised in several subsequent publications. Rapid accumulation of novel genomic data and the advancements in phylogenetics now facilitate a robust and precise foundation for the higher-level classification within the kingdom. This study provides an updated classification of the kingdomFungi, drawing upon a comprehensive phylogenomic analysis ofHolomycota, with which we outline well-supported nodes of the fungal tree and explore more contentious groupings. We accept 19 phyla ofFungi,viz. Aphelidiomycota,Ascomycota,Basidiobolomycota,Basidiomycota,Blastocladiomycota,Calcarisporiellomycota,Chytridiomycota,Entomophthoromycota,Entorrhizomycota,Glomeromycota,Kickxellomycota,Monoblepharomycota,Mortierellomycota,Mucoromycota,Neocallimastigomycota,Olpidiomycota,Rozellomycota,Sanchytriomycota,andZoopagomycota. In the phylogenies,Caulochytriomycotaresides inChytridiomycota; thus, the former is regarded as a synonym of the latter, whileCaulochytriomycetesis viewed as a class inChytridiomycota. We provide a description of each phylum followed by its classes. A new subphylum,SanchytriomycotinaKarpov is introduced as the only subphylum inSanchytriomycota. The subclassPneumocystomycetidaeKirk et al. inPneumocystomycetes,Ascomycotais invalid and thus validated. Placements of fossil fungi in phyla and classes are also discussed, providing examples. 

Organic photovoltaics have achieved breakthroughs in power conversion efficiency due to the superior aggregation and packing nature of non‐fullerene acceptors (NFAs). Solution processing and various treatments would tend to form distinct packing motifs for state‐of‐the‐art NFAs. Herein, the solvent‐induced polymorphism for 3,9‐bis(2‐methylene‐(3‐(1,1‐dicyanomethylene)‐indanone))‐5,5,11,11‐tetrakis(4‐hexylphenyl)‐dithieno[2,3‐d:2′,3′‐d′]‐s‐indaceno[1,2‐b:5,6‐b′]dithiophne) (ITIC) prepared by chloroform (CF) and chlorobenzene (CB) is revealed. The packing motif of ITIC exhibits dense π–π stacking from CF induction, which presents red‐shifted absorption and reversible high‐temperature crystallization and melting. Meanwhile, strong lamellar stacking and π–π stacking can be formed in the CB solution with unstable low‐temperature crystallization and melting. Combining in situ absorption spectra and interaction calculation, the stronger preaggregation of ITIC in the CF solution was found to be the main reason for forming a different packing motif from in the CB solution. The packing and thermodynamic features are retained in the PBDB‐T:ITIC blends, though good miscibility weakens characteristic features. Benefiting from the polymorph structure, CB‐processed devices denote more favorable performance but less thermal stability. This research indicates the significant effect of solvent induction for manipulating and optimizing the morphology of organic solar cell devices.