Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition

This paper does not attempt to design a state-of-the-art method for visual recognition but investigates a more efficient way to make use of convolutions to encode spatial features. By comparing the design principles of the recent convolutional neural networks ConvNets) and Vision Transformers, we propose to simplify the self-attention by leveraging a convolutional modulation operation. We show that such a simple approach can better take advantage of the large kernels (>=7x7) nested in convolutional layers. We build a family of hierarchical ConvNets using the proposed convolutional modulation, termed Conv2Former. Our network is simple and easy to follow. Experiments show that our Conv2Former outperforms existent popular ConvNets and vision Transformers, like Swin Transformer and ConvNeXt in all ImageNet classification, COCO object detection and ADE20k semantic segmentation

Poly[tetra­aquadi-μ4-oxalato-potassium­ytterbium(III)]

In the title compound, [KYb(C2O4)2(H2O)4]n, the YbIII ion lies on a site of symmetry in a dodeca­hedral environment defined by eight O atoms from four oxalate ligands. The K atom lies on a different axis and is coordinated by four O atoms from four oxalate ligands and four water O atoms. The oxalate ligand has an inversion center at the mid-point of the C—C bond. The metal ions are linked by the oxalate ligands into a three-dimensional framework. O—H⋯O hydrogen bonding is present in the crystal structure

Poly[tetra­aquadi-μ4-oxalato-lutetium(III)potassium]

In the title compound, [KLu(C2O4)2(H2O)4]n, the LuIII ion lies on a site of symmetry in a dodeca­hedron defined by eight O atoms from four oxalate ligands. The K atom lies on another site of the same symmetry and is coordinated by four oxalate O atoms and four O water atoms. The mid-point of the C—C bond of the oxalate group lies on an inversion center. In the packing structure, each oxalate ligand links two Lu(III) and two K atoms, forming a three-dimensional open framework with channels running along [001]. Inter­molecular O—H⋯O hydrogen bonds occur

The draft genome of the transgenic tropical fruit tree papaya ( Carica papaya Linnaeus)

Papaya, a fruit crop cultivated in tropical and subtropical regions, is known for its nutritional benefits and medicinal applications. Here we report a 3 draft genome sequence of \u27SunUp\u27 papaya, the first commercial virus-resistant transgenic fruit tree1 to be sequenced. The papaya genome is three times the size of the Arabidopsis genome, but contains fewer genes, including significantly fewer disease-resistance gene analogues. Comparison of the five sequenced genomes suggests a minimal angiosperm gene set of 13,311. A lack of recent genome duplication, atypical of other angiosperm genomes sequenced so far may account for the smaller papaya gene number in most functional groups. Nonetheless, striking amplifications in gene number within particular functional groups suggest roles in the evolution of tree-like habit, deposition and remobilization of starch reserves, attraction of seed dispersal agents, and adaptation to tropical daylengths. Transgenesis at three locations is closely associated with chloroplast insertions into the nuclear genome, and with topoisomerase I recognition sites. Papaya offers numerous advantages as a system for fruit-tree functional genomics, and this draft genome sequence provides the foundation for revealing the basis of Carica\u27s distinguishing morpho-physiological, medicinal and nutritional properties

Delving Deeper into Data Scaling in Masked Image Modeling

Understanding whether self-supervised learning methods can scale with unlimited data is crucial for training large-scale models. In this work, we conduct an empirical study on the scaling capability of masked image modeling (MIM) methods (e.g., MAE) for visual recognition. Unlike most previous works that depend on the widely-used ImageNet dataset, which is manually curated and object-centric, we take a step further and propose to investigate this problem in a more practical setting. Specifically, we utilize the web-collected Coyo-700M dataset. We randomly sample varying numbers of training images from the Coyo dataset and construct a series of sub-datasets, containing 0.5M, 1M, 5M, 10M, and 100M images, for pre-training. Our goal is to investigate how the performance changes on downstream tasks when scaling with different sizes of data and models. The study reveals that: 1) MIM can be viewed as an effective method to improve the model capacity when the scale of the training data is relatively small; 2) Strong reconstruction targets can endow the models with increased capacities on downstream tasks; 3) MIM pre-training is data-agnostic under most scenarios, which means that the strategy of sampling pre-training data is non-critical. We hope these observations could provide valuable insights for future research on MIM

2-(2-Hy­droxy-3-meth­oxy­phen­yl)-1H-benzimidazol-3-ium perchlorate

In the title mol­ecular salt, C14H13N2O2 +·ClO4 −, the ring systems in the cation are almost coplanar [dihedral angle = 5.53 (13)°]. Intra­molecular N—H⋯O and O—H⋯O hydrogen bonds generate S(6) and S(5) rings, respectively. In the crystal, the two H atoms involved in the intra­molecular hydrogen bonds also participate in inter­molecular links to acceptor O atoms of the perchlorate anions. A simple inter­molecular N—H⋯O bond also occurs. Together, these form a double-chain structure along [101]

Aqua­cyanido{6,6′-dimeth­oxy-2,2′-[1,2-phenyl­enebis(nitrilo­methanylyl­idene)]diphenolato}cobalt(III) acetonitrile hemisolvate

In the title complex, [Co(C22H18N2O4)(CN)(H2O)]·0.5CH3CN, the CoIII cation is N,N′,O,O′-chelated by a 6,6′-dimeth­oxy-2,2′-[1,2-phenyl­enebis(nitrilo­methanylyl­idene)]diphenolate dianion, and is further coordinated by a cyanide anion and a water mol­ecule in the axial sites, completing a distorted octa­hedral coordination geometry. In the crystal, pairs of bifurcated O—H⋯(O,O) hydrogen bonds link adjacent mol­ecules, forming centrosymmetric dimers. The acetonitrile solvent mol­ecule shows 0.5 occupancy