218 research outputs found
Linear Progressive Coding for Semantic Communication using Deep Neural Networks
We propose a general method for semantic representation of images and other
data using progressive coding. Semantic coding allows for specific pieces of
information to be selectively encoded into a set of measurements that can be
highly compressed compared to the size of the original raw data. We consider a
hierarchical method of coding where a partial amount of semantic information is
first encoded a into a coarse representation of the data, which is then refined
by additional encodings that add additional semantic information. Such
hierarchical coding is especially well-suited for semantic communication i.e.
transferring semantic information over noisy channels. Our proposed method can
be considered as a generalization of both progressive image compression and
source coding for semantic communication. We present results from experiments
on the MNIST and CIFAR-10 datasets that show that progressive semantic coding
can provide timely previews of semantic information with a small number of
initial measurements while achieving overall accuracy and efficiency comparable
to non-progressive methods
On Gosper's Pi(q) and Lambert series identities
In an interesting article entitled `Experiments and discoveries in q-trigonometry'', R. W. Gosper conjectured few beautiful Pi(q) and Lambert series identities. Many people have attempted confirming some of those identities in the Gosper's list, mainly by using Gosper's q-trigonometric identities. In this paper we either prove or disprove all the Pi(q) and Lambert series identities in the Gosper's list by mainly using S. Ramanujan's theta function identities and W. N. Bailey's summation formula. In the process, we obtain three new Gosper kind of identities
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