This document describes how the G-ACh may be used to provide the infrastructure that forms part of the Management Communication Network (MCN) and a Signaling Communication Network (SCN). Collectively, the MCN and SCN may be referred to as the Data Communication Network (DCN). This document explains how MCN and SCN messages are encapsulated, carried on the G-ACh, and demultiplexed for delivery t...

With the availability of large amounts of documents and multimedia content to be classified, the creation of new databases with labeled examples is an expensive task. Efficient supervised classifiers often require large training databases that are not always immediately available. Active learning approaches solve this issue by querying an expert to set a label to particular instances. In this p...

Background: Quality assurance in the hematology laboratory is a must to ensure laboratory users of reliable test results with high degree of precision and accuracy. Even after so many advances in hematology laboratory practice, pre-analytical errors remain a challenge for practicing pathologists. This study was undertaken with an objective to ...

Many computer vision problems such as object segmentation or reconstruction can be formulated in terms of labeling a set of pixels or voxels. In certain scenarios, we may know the number of pixels or voxels which can be assigned to a particular label. For instance, in the reconstruction problem, we may know size of the object to be reconstructed. Such label count constraints are extremely power...

Partial label learning aims to learn a multi-class classifier, where each training example corresponds set of candidate labels among which only one is correct. Most studies in the space have focused on difference between and non-candidate labels. So far, however, there has been little discussion about correlation partial learning. This paper begins with research correlation, followed by establi...

Regularization of (deep) learning models can be realized at the model, loss, or data level. As a technique somewhere in-between loss and data, label smoothing turns deterministic class labels into probability distributions, for example by uniformly distributing certain part mass over all classes. A predictive model is then trained on these distributions as targets, using cross-entropy function....

In this paper we introduce remainder cordial labeling of graphs. Let $G$ be a $(p,q)$ graph. Let $f:V(G)rightarrow {1,2,...,p}$ be a $1-1$ map. For each edge $uv$ assign the label $r$ where $r$ is the remainder when $f(u)$ is divided by $f(v)$ or $f(v)$ is divided by $f(u)$ according as $f(u)geq f(v)$ or $f(v)geq f(u)$. The function$f$ is called a remainder cordial labeling of $G$ if $left| e_{...