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Enlighted by subdomain adaptation methods, this paper designs a novel subdomain adaptative deep network (SADN) for excavating diagnosis knowledge from source domain datasets. Firstly, the convolutional layer, residual blocks and SE-Residual blocks are utilized for extracting meaningful deep features ...
: In order to illustrate different notions presented in this paper, we will consider an example in the intrusion detection field (the small example is extracted from KDD′99 data sets [11]). The training set given in Table 1 is composed of an extract of different connections corresponding to...
A 2022 paper by Li et al. [119] proposes a model named “Meta-IP” to deal with CI in the task of project extension forecasting; this model employs a transfer learning strategy framed as a LSL setting: a simulated source dataset is used as the support set for MAML, while the (real-...
Peptide hormones and neuropeptides are signaling molecules that control diverse aspects of mammalian homeostasis and physiology. Here we provide evidence for the endogenous presence of a sequence diverse class of blood-borne peptides that we call “cappe
Beads were washed five times in an IP buffer. Washed beads were directly resuspended in Sample Buffer Laemmli 2x (Sigma S3401), and boiled at 95 °C for 10 min. DNA–FISH on metaphase spreads DNA–FISH was performed as previously described32,38. Slides were treated with RNase A for...
Each sample was solubilized in 12 μL 97:3 water: acetonitrile with 0.1% formic acid. Samples were loaded using split-less nano-Ultra Performance Liquid Chromatography (10 kpsi nanoAcquity; Waters, Milford, MA, USA). The mobile phase was: A) H2O + 0.1% formic acid and B) acetonitrile +...
Other motives are added in this paper regarding several ensemble learners, errors, sampling techniques, and feature selection techniques for refactoring prediction at the class level. Objective: This work aims to develop an ensemble-based refactoring prediction model with structural identification of ...
The paper is as follows: Section 2 provides the essential functions of the Sec-G class, with a focus on the Kum-W distribution as the baseline. The main properties of the class are discussed in Section 3. Applications to simulated and real-life data are given in Section 4. Some concludin...
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