Again a caveat — these are the talks in which I took reasonable enough notes to write anything coherent.
Green Communication: From Maxwell’s Demon to “Informational Friction”
Pulkit Grover
Pulkit talked about trying to tie a physical interpretation the energy used in communication during computation. Physicists might argue that reversible computation costs nothing, but this ignores friction and noise. Pulkit discussed a simple network model to account for “informational friction” that penalizes the bit-distance product in communicating on a chip. See also Pulkit’s short video on the topic.
Energy Harvesting Receivers
Hajar Mahdavi-Doost, Roy Yates
Roy talked about a model in which receivers have to harvest the energy they need for sampling/buffering/decoding the transmissions. These three tasks cost different amounts, and in particular, the rate at which the receiver samples the output dictates the other parameters. The goal is to choose a rate which helps meet the decoder energy requirements. Because the receiver has to harvest the energy it needs, it has to design a policy to switch between the three operations while harvesting the (time-varying) energy available to it.
Multiple Access and Two-way Channels with Energy Harvesting and Bidirectional Energy Cooperation
Kaya Tutuncuoglu Aylin Yener
Unlike the previous talk, this was about encoders which have to transmit energy to the receivers — there’s a tradeoff between transmitting data and energy, and in the MAC and TWC there is yet another dimension in how the two users can cooperate. For eample, they can cooperate in energy transmission but not data cooperation. There were a lot of results in here, but there was also a discussion of policies for the users. In particular a “procrastination” strategy turns out to work well (rejoice!).
An equivalence between network coding and index coding
Michelle Effros, Salim El Rouayheb, Michael Langberg
The title says it all! For every network coding problem (multiple unicast, multicast, whatever), there exists a corresponding index coding problem (constructed via a reduction) such that a solution to the latter can be easily translated to a solution for the former. This equivalence holds for all network coding problems, not just linear ones.
Crowd-sourcing epidemic detection
Constantine Caramanis, Chris Milling, Shie Mannor, Sanjay Shakkottai
Suppose we have a graph and we can see some nodes are infected. This paper was on trying to distinguish between whether the infected nodes started from a single point infection spread via an SI model, or just from a random pattern of infection. They provide two algorithms for doing this and then address how to deal with false positives using ideas from robust statistics.
.
and 
is there a term we know that 
where 
and 
are isotropic Gaussian vectors with the same differential entropy as 
and 
with spherically symmetric decreasing densities and show that the differential entropy of their sum lies between the two terms in the regular EPI.
files, 
users and a size 
cache at each user, how should they cache files so as to best allow a broadcaster to share the bandwidth to them? More simply, suppose there are three people who may want to watch one of three different TV shows, and they can buffer the content of one TV show. Since a priori you don’t know which show they want to watch, the idea might be to buffer/cache the first 3rd of each show at each user. They show that this is highly suboptimal. Because the content provider can XOR parts of the content to each user, the caching strategy should not be the same at each user, and the real benefit is the global cache size.
on a discrete set. The thing is we only have a handle on the entropy 
but not on the distribution itself.
drawn i.i.d. from ![\mathbb{E}[M] \le \frac{m H(P)}{\log m} + 1](https://s0.wp.com/latex.php?latex=%5Cmathbb%7BE%7D%5BM%5D+%5Cle+%5Cfrac%7Bm+H%28P%29%7D%7B%5Clog+m%7D+%2B+1&bg=ffffff&fg=333333&s=0&c=20201002)
be the probabilities of the different elements. There could be countably many, but we can write the expectation of the number of unique elements as![\mathbb{E}[M] = \sum_{i=1}^{\infty} (1 - (1 - p_i)^m)](https://s0.wp.com/latex.php?latex=%5Cmathbb%7BE%7D%5BM%5D+%3D+%5Csum_%7Bi%3D1%7D%5E%7B%5Cinfty%7D+%281+-+%281+-+p_i%29%5Em%29&bg=ffffff&fg=333333&s=0&c=20201002)
drawn i.i.d. from 
An Ergodic Walk 