21:I[432972,["/_next/static/chunks/bb923814e1f4d2e9.js","/_next/static/chunks/dd3ee9f5b7716ca6.js","/_next/static/chunks/4204a0ae82542ca8.js","/_next/static/chunks/1270dead247da766.js","/_next/static/chunks/6e1aa89a642f40be.js","/_next/static/chunks/2d7aa753e81324a8.js","/_next/static/chunks/8ffd0b71567920d8.js","/_next/static/chunks/67ea6dfd21f16c5f.js","/_next/static/chunks/24eb4379f68013fd.js","/_next/static/chunks/b8cd775ffdf935bd.js","/_next/static/chunks/983103cd9eb7b428.js","/_next/static/chunks/263f2cdcb9aa2341.js","/_next/static/chunks/294b9525248c8e5f.js"],"default"] 22:T536,Unsupervised domain adaptation (UDA) has been highly successful in transferring knowledge acquired from a label-rich source domain to a label-scarce target domain. Open-set domain adaptation (open-set DA) and universal domain adaptation (UniDA) have been proposed as solutions to the problem concerning the presence of additional novel categories in the target domain. Existing open-set DA and UniDA approaches treat all novel categories as one unified unknown class and attempt to detect this unknown class during the training process. However, the features of the novel categories learned by these methods are not discriminative. This limits the applicability of UDA in the further classification of these novel categories into their original categories, rather than assigning them to a single unified class. In this paper, we propose a self-labeling framework to cluster all target samples, including those in the ''unknown'' categories. We train the network to learn the representations of target samples via self-supervised learning (SSL) and to identify the seen and unseen (novel) target-sample categories simultaneously by maximizing the mutual information between labels and input data. We evaluated our approach under different DA settings and concluded that our method generally outperformed existing ones by a wide margin.23:T4bd,Cheyletus malaccensis Oudemans is a predatory mite inhabiting grain depots in China. The relationship between temperature and the population growth rate of C. malaccensis is useful for predicting its population dynamics. Age-stage, two-sex life tables of the predator, C. malaccensis, reared on Acarus siro were constructed under laboratory conditions at 22, 24, 28, 30, and 32 °C, 75% relative humidity, and a 0:24 h (L:D) photoperiod. Increasing temperature shortened the development time of the immature stages. The complete generation time of C. malaccensis ranged from 11.10 d to 27.50 d. Life table parameters showed that 28 °C was the optimum temperature for the growth and development of C. malaccensis; populations could increase rapidly at this temperature. The highest net reproductive rate (R₀ = 290.25) and highest fecundity (544.52) occurred at 28 °C. Temperature significantly affected the intrinsic rate of increase (r), fecundity, and finite rate of increase (λ). The values of age-specific fecundity (high to low) were 28 °C > 24 °C > 30 °C > 32 °C > 22 °C, while the values of age-stage-specific fecundity had the same trend.24:T55e,

#	Concept	H-Index	Publications	Citations
1	Engineering	2	2	59
2	Computer Science	1	1	21
3	Data Science	1	1	21
4	Data Mining	1	1	21
5	Deep Learning	1	1	21

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