236 research outputs found
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 remains the only stable compound in this pressure
range. The pressure-induced transformations are predicted as
, which occur at 24, 139, 204 and 349 GPa, respectively.
and structures are layered polytypes based on close
packings of H atoms with Be atoms filling octahedral voids in alternating
layers. and structures have 3D-networks of strong bonds, but
also feature rectanular and squre, respectively, layers of H atoms with short
H-H distances. and are semiconductors while and
are metallic. We have explored superconductivity of both metallic
phases, and found large electron-phonon coupling parameters of =0.63
for (resulting in a of 32.1-44.1 K) at 250 GPa and =0.65 for ( = 46.1-62.4 K) at 400 GPa. The dependence of
on pressure indicates that initially increases to a maximum of 45.1 K for
at 275 GPa and 97.0 K for 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
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