Exploration of stable compounds, crystal structures, and superconductivity in the Be-H system

Using first-principles variable-composition evolutionary methodology, we explored the high-pressure structures of beryllium hydrides between 0 and 400 GPa. We found that BeH$_2$ remains the only stable compound in this pressure range. The pressure-induced transformations are predicted as $Ibam$ $\rightarrow$ $P\bar{3}m1$ $\rightarrow$ $R\bar{3}m$ $\rightarrow$ $Cmcm$ $\rightarrow$ $P4/nmm$, which occur at 24, 139, 204 and 349 GPa, respectively. $P\bar{3}m1$ and $R\bar{3}m$ structures are layered polytypes based on close packings of H atoms with Be atoms filling octahedral voids in alternating layers. $Cmcm$ and $P4/nmm$ structures have 3D-networks of strong bonds, but also feature rectanular and squre, respectively, layers of H atoms with short H-H distances. $P\bar{3}m1$ and $R\bar{3}m$ are semiconductors while $Cmcm$ and $P4/nmm$ are metallic. We have explored superconductivity of both metallic phases, and found large electron-phonon coupling parameters of $\lambda$=0.63 for $Cmcm$ (resulting in a $T_c$ of 32.1-44.1 K) at 250 GPa and $\lambda$=0.65 for $P4/nmm$ ($T_c$ = 46.1-62.4 K) at 400 GPa. The dependence of $T_c$ on pressure indicates that $T_c$ initially increases to a maximum of 45.1 K for $Cmcm$ at 275 GPa and 97.0 K for $P4/nmm$ at 365 GPa, and then decreases with increasing pressure for both phases

4-[(5-Chloro-3-methyl-1-phenyl-1H-pyrazol-4-yl)methyl­idene­amino]-1,5-dimethyl-2-phenyl-1H-pyrazol-3(2H)-one

In the mol­ecule of the title compound, C22H20ClN5O, the atoms of the two pyrazole rings and the –C=N– group which joins them are essentially coplanar, with an r.m.s. deviation of 0.054 (2) Å. The phenyl rings form dihedral angles of 41.24 (5) and 55.53 (5)° with this plane. The crystal structure is stabilized by weak inter­molecular π–π inter­actions, with centroid-to-centroid distances of 3.6179 (13) Å between the imidazole rings

1,5-Dimethyl-4-{[(3-methyl-5-oxo-1-phenyl-4,5-dihydro-1H-pyrazol-4-yl­idene)(thio­phen-2-yl)meth­yl]amino}-2-phenyl-1H-pyrazol-3(2H)-one

In the title compound, C26H23N5O2S, an intra­molecular N—H⋯O inter­action generates an S(6) ring. The essentially planar S(6) and pyrazole rings [maximum deviations = −0.0270 (14) and 0.0195 (15) Å, respectively] are nearly coplanar, making a dihedral angle of 3.94 (6)°. The S(6) ring makes dihedral angles of 23.79 (6), 78.53 (6) and 67.91 (6)° with the pyrazolone ring, the pyrazole ring and the benzene ring of anti­pyrine, respectively. The structure exhibits a thienyl-ring flip disorder with occupancy factors in the ratio 0.82:0.18

Gradual Network for Single Image De-raining

Most advances in single image de-raining meet a key challenge, which is removing rain streaks with different scales and shapes while preserving image details. Existing single image de-raining approaches treat rain-streak removal as a process of pixel-wise regression directly. However, they are lacking in mining the balance between over-de-raining (e.g. removing texture details in rain-free regions) and under-de-raining (e.g. leaving rain streaks). In this paper, we firstly propose a coarse-to-fine network called Gradual Network (GraNet) consisting of coarse stage and fine stage for delving into single image de-raining with different granularities. Specifically, to reveal coarse-grained rain-streak characteristics (e.g. long and thick rain streaks/raindrops), we propose a coarse stage by utilizing local-global spatial dependencies via a local-global subnetwork composed of region-aware blocks. Taking the residual result (the coarse de-rained result) between the rainy image sample (i.e. the input data) and the output of coarse stage (i.e. the learnt rain mask) as input, the fine stage continues to de-rain by removing the fine-grained rain streaks (e.g. light rain streaks and water mist) to get a rain-free and well-reconstructed output image via a unified contextual merging sub-network with dense blocks and a merging block. Solid and comprehensive experiments on synthetic and real data demonstrate that our GraNet can significantly outperform the state-of-the-art methods by removing rain streaks with various densities, scales and shapes while keeping the image details of rain-free regions well-preserved.Comment: In Proceedings of the 27th ACM International Conference on Multimedia (MM 2019

Diverse Cotraining Makes Strong Semi-Supervised Segmentor

Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it. In this work, we revisit the core assumption that supports co-training: multiple compatible and conditionally independent views. By theoretically deriving the generalization upper bound, we prove the prediction similarity between two models negatively impacts the model's generalization ability. However, most current co-training models are tightly coupled together and violate this assumption. Such coupling leads to the homogenization of networks and confirmation bias which consequently limits the performance. To this end, we explore different dimensions of co-training and systematically increase the diversity from the aspects of input domains, different augmentations and model architectures to counteract homogenization. Our Diverse Co-training outperforms the state-of-the-art (SOTA) methods by a large margin across different evaluation protocols on the Pascal and Cityscapes. For example. we achieve the best mIoU of 76.2%, 77.7% and 80.2% on Pascal with only 92, 183 and 366 labeled images, surpassing the previous best results by more than 5%.Comment: ICCV2023, Camera Ready Version, Code: \url{https://github.com/williamium3000/diverse-cotraining