We note first that since the class labels of many of the training examples are unknown, it is not possible to carry out a one-vs-one strategy.
How c-Myc mechanistically stimulates epidermal cells to differentiate, as opposed to undergo apoptosis for example, is unknown.
In the absence of what is now considered basic knowledge of sanitation and health (the role of bacteria in disease, for example, was unknown until the 19th century), mortality rates were very high, especially for infants and children.
To illustrate how the contraction bias could emerge from such a Bayesian computation, we consider the following three examples: L1 is unknown, L2 is known.
Aside from these few examples, it is unknown whether most ecologically important diatoms with cosmopolitan distributions are true species or whether they are comprised of multiple cryptic species.
For example, it is unknown how much transfer of knowledge occurred between the intervention teachers and the comparison group teachers.
For example, it is unknown whether blocking CGRP could potentially transform transient mild cerebral ischemia into a full-blown brain infarct [9] and whether these risks are higher in women [9, 84].
For example, it is unknown how NP size, surface chemistry, or cell type will impact the magnitude of spectral change or broadening of the NP spectra in a cellular environment.
For example, it is unknown whether mating takes place.
For example, it is unknown whether low-abundance microbes are restricted to particular environments or universally dispersed [6].
