I agree on the ImageNet and I also think they should publish their results on that dataset as well. I don't see why it shouldn't perform well.
CIFAR10 is small compared to ImageNet, However, achieving highly competitive accuracy is very hard.
Nearly all SOTA architectures have more than 20 ~ 50 Millions of parameters or high memory usage. So I still think their result is impressive enough concerning their "small and simple" architecture with that amount of parameters. If they were to use any new/fancy techniques and achieve the current accuracy, I would say you are right, but concerning everything that's been said, its impressive for its kind.
Its as if, a normal car is already on par with top sports car which utilized all they could at hand to achieve their maximum performance. Think what happens if an already well-performing car gets tuned up with those fancy techniques its rivals are utilizing. (The main idea for the paper by the way)

Again we need to pay attention here that, models such as CondenseNet cannot be compared with their architecture in that sense because the nature of the work is completely different.
It seems you failed at getting their actual point concerning strided/max-pooling. Strided convolution always requires more parameters, it imposes more overhead compared to the pooling operation. Were you to have a limited budget concerning FLOPS/#params, you would clearly see what difference such trivial yet important statement makes.

Concerning the ImageNet, while I agree with you that they should also submit their results on ImageNet, I do not think failing to do so would disqualify them completely! Since the nature of the work implies you can improve "any" architecture with the aforementioned principles. (ResNet and SqueezeNet were two simple examples in their paper with a single change!) So you would still be benefiting from their work even if you are not using their demo architecture.
Aside from that, they are trying to provide better understanding as to why certain things work better, so that with such gained intuition, a better strategy, a method is devised or developed.(The reason for using dropout and their effect, the use of maxpooling, the PLD, PLS issues, etc) So unlike CondenseNet, they were not focused on a specific technique to improve, rather they tried to focus on the main building blocks that affect the performance the most and why. and in choosing those building blocks, they aimed at the most primitive ones that nearly all architectures can take advantage of. So Simply put, you may start improving the already great CondenseNet or pretty any architecture by following such principles.

I could think of a reason as to why they may not have included the CondenseNet results. Looking at their comment on the arXiv page, it seems they had submitted their paper in December 2017 and also looking at the date CondensNet was published,(November 25, 2017) its possible by December they were already in the process of submitting their work and never noticed CondenseNet.

They first dismissed the previous claim that advocated: "use overlapped pooling because of the imagelike properties and also because the non-overlapped pooling loses a lot of information in that sense which affects the performance adversely", then they indicated that when considering their intuition and what actually exists, looking at their experiments proved the non-overlapped pooling performing better. Their provided intuition seems plausible, but at the same time it may not apply to all situations.