403 research outputs found
On -quasinormal subgroups of finite groups
Let be a finite group and some
partition of the set of all primes , that is, , where and for all . We say that is -primary
if is a -group for some . A subgroup of is said to
be: -subnormal in if there is a subgroup chain such that either
or is -primary for all ,
modular in if the following conditions hold: (i) for all such that , and (ii) for
all such that . In this paper, a subgroup of
is called -quasinormal in if is modular and
-subnormal in . We study -quasinormal subgroups of . In
particular, we prove that if a subgroup of is -quasinormal in
, then for every chief factor of between and the
semidirect product is -primary.Comment: 9 page
(R,R)-4,4′-Dibromo-2,2′-[cycloÂhexane-1,2-diylbis(nitriloÂmethylÂidyne)]diphenol
The molÂecule of the title compound, C20H20Br2N2O2, lies on a twofold axis. It contains two stereogenic C atoms with R chirality and thus it is the enatiomerically pure R,R-diastereomer. There is an intraÂmolecular O—H⋯N hydrogen bond
Localization Transformation of Five Coordinate Milling Machine
AbstractAs five coordinates gantry milling machine is not able to meet the requirements of the parts of precision and efficient processing, for the machine tool's electrical function degradation, mechanical part aging, CNC system backward, now it needs to upgrade the whole electrification and fix the mechanical part by Huangzhong, HNC - 848 c/M bus type numerical control system. Though the machine localization reformation, the precision and efficiency of the machine tool are improved, thus the application of domestic CNC system and functional components in the machine tool are promoted
Severe dopaminergic neuron loss in rhesus monkey brain impairs morphine-induced conditioned place preference
On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning
Though Self-supervised learning (SSL) has been widely studied as a promising
technique for representation learning, it doesn't generalize well on
long-tailed datasets due to the majority classes dominating the feature space.
Recent work shows that the long-tailed learning performance could be boosted by
sampling extra in-domain (ID) data for self-supervised training, however,
large-scale ID data which can rebalance the minority classes are expensive to
collect. In this paper, we propose an alternative but easy-to-use and effective
solution, Contrastive with Out-of-distribution (OOD) data for Long-Tail
learning (COLT), which can effectively exploit OOD data to dynamically
re-balance the feature space. We empirically identify the counter-intuitive
usefulness of OOD samples in SSL long-tailed learning and principally design a
novel SSL method. Concretely, we first localize the `head' and `tail' samples
by assigning a tailness score to each OOD sample based on its neighborhoods in
the feature space. Then, we propose an online OOD sampling strategy to
dynamically re-balance the feature space. Finally, we enforce the model to be
capable of distinguishing ID and OOD samples by a distribution-level supervised
contrastive loss. Extensive experiments are conducted on various datasets and
several state-of-the-art SSL frameworks to verify the effectiveness of the
proposed method. The results show that our method significantly improves the
performance of SSL on long-tailed datasets by a large margin, and even
outperforms previous work which uses external ID data. Our code is available at
https://github.com/JianhongBai/COLT
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