5 with theoretical density f1(Cox(2,3,0.05)) (resp. f2(Weibull(1.2,2))), that the error, being important at the start, decreases speedily for the values of ϵ in the neighborhood of the lower bound. This may be explained by the fact that they are at the boundary of the stability...
Although this trained model is independent of input data (as explained above), the model can still perform well on the training phase and achieve small training errors in the limit of small regularization. This is because the training output data is trivially ‘cooked’ into the model via the...
B73 kernels typically have a dented, collapsed starchy crown, with most of the hard, vitreous endosperm located on the abgerminal kernel side. In contrast, the top offka1-1kernels is convex and vitreous endosperm forms on both the abgerminal and adgerminal sides of the kernel (Fig.1b). W...
We applied KDDE to achieve this, as explained in the following. Let Y ij denote the bivariate observation of the i-th subject in the j-th day (j = 1 or 2). The entries are log-sodium (first) and log-potassium (last). 146 Guillermo Basulto-Elias et al. Figure 8: Scatterplot of...
The integration of QTL mapping and genome-wide association analysis (GWAS) identified two SNPs, 8_166371888 and 8_178656036, which overlapped the QTL interval ofqSC8-1, identified in the tropical maize line YML46. The phenotypic variance explained (PVE) by the QTLqSC8-1was12.17%, while the...
Data generated from metabolomics experiments are different from other types of “-omics” data. For example, a common phenomenon in mass spectrometry (MS)-based metabolomics data is that the data matrix frequently contains missing values, which complicat
The rest of this paper is organized as follows: in Section 2, the related work is discussed as the background for the present paper’s proposal; in Section 3, the SOM algorithm is described; in Section 4, the representation of the user data is described and explained; in Section 5, the...
Among them, AX-86284808 and AX-91425354 were detected by two and four methods, respectively, which explained up to 32.12% of the phenotypic variation. When r2 = 0.1 and the attenuation distance of LD was approximately 200 kb, AX-86284808 and AX-91425354 were mapped onto B73 Ref Gen_...
This preference may be explained by the fact that an RBF kernel produces smooth functions and cannot capture non-differentiable points, such as the triangle’s vertex. To test this hypothesis, we derived an additional simulation setting where the conditional expectation is quadratic and U-shaped. ...
This phenomenon can be explained with the condition specified in Eq. (5). We contend that the root cause of this bias is the use of a data-independent kernel. We show that using Isolation Kernel—because it adapts its similarity to local density—the resultant T-AHC has less density bias...