**The Gaussian Channel with Noisy Feedback: Near-Capacity Performance Via Simple Interaction**

*Assaf Ben-Yishai, Ofer Shayevitz*

This was a really nice talk by Ofer on trying to get practical codes for AWGN channels with noisy feedback by using the intuition given by the Schalkwijk-Kailath scheme plus some tricks from using the mod operation. This is reminiscent of lattices (which may be an interesting future direction). The SK scheme has a problem with noise accumulation, which they deal with using these mode operations, and can get to errors around 10^(-6) with around 19 rounds, or blocklength 19 at reasonable SNRs. Blocklength is misleading here since there is feedback every symbol. The other catch is that the feedback link must have much higher SNR than the forward link, but this is true in applications such as sensing, where the receiver may be plugged into the wall, but the transmitter may be on a swallowable medical monitoring device.

**Point-To-Point Codes for Interference Channels: A Journey Toward High Performance at Low Complexity**

*Young-Han Kim*

Continuing with my UCSD bias, I also wanted to mention Young-Han’s talk, which was on using COTS (commercial, off-the-shelf) coding schemes on the interference channel (in particular, the 2 user IC). He talked about rate splitting approaches and block Markov schemes. Much of this work is with Lele Wang, who may be graduating soon…

**Signal Detection on Graphs**

*Venkatesh Saligrama*

This was a hypothesis testing problem where the observations come from nodes on graph. Under the null, they are Gaussian noise, and under the other hypothesis, there is a connected subgraph with an elevated mean. How should we do detection in this scenario? This is a compound hypothesis testing problem because there are (too) many possible connected subgraphs to consider. He gets around this by looking at a convex program parameterized by a measure of the size/shape of the connected component. This is where my notes get messy though, so you might want to look at the paper if it sounds interesting to you…

**Hypercontractivity in Hamming Space**

*Yury Polyanskiy*

I’ve hypercontractivity before, and Yury talked about his paper on ArXiV, which is about functions on the binary hypercube. This talk felt more like a tour of results on hypercontractivity and less like a “here is my new result” talk, which I actually appreciated because I felt it tied together ideas well and made me realize how strange the hypercontractivity parameter of an operator is.

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- Steven M. Kay, Fundamentals of Statistical Signal Processing – Estimation Theory (Vol. 1), Prentice Hall, 1993.
- H. Vincent Poor, An Introduction to Signal Detection and Estimation, 2nd Edition, Springer, 1998.
- Harry L. Van Trees, Detection, Estimation, and Modulation Theory (in 4 parts), Wiley, 2001 (a reprint).
- M.D. Srinath, P.K. Rajasekaran, P. K. and R. Viswanathan, Introduction to Statistical Signal Processing with Applications, Prentice Hall, 1996.

Detection and estimation is a fundamental class for the ECE graduate curriculum, but these “standard” textbooks are around 20 years old, and I can’t help but think there might be more “modern” take on the subject (no I’m not volunteering). Venu Veeravalli‘s class doesn’t use a book, but just has notes. However, I think the students at Rutgers (majority MS students) would benefit from a textbook, at least as a grounding.

Srinath et al. is what my colleague Narayan Mandyam uses. Kay is what I was leaning to before (because it seems to be the most widely used), but Poor’s book is the one I read. I think I am putting up the Van Trees as a joke, mostly. I mean, it’s a great book but I think a bit much for a textbook. So what do the rest of you use? Also, if you are teaching this course next semester, perhaps we can share some ideas. I think the curriculum might be ripe for some shaking up. If not in core material, at least in the kinds of examples we use. For example, I’m certainly going to cover differential privacy as a connection to hypothesis testing.

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Suppose you see a car on the Turnpike who is clearly driving dangerously (weaving between cars, going 90+ MPH, tailgating an ambulance, and the like). You have to decide whether the car has New Jersey or New York plates [*]?

This is a hypothesis testing problem. I will assume for simplicity that New York drivers have cars with New York plates and New Jersey drivers have New Jersey plates [**]:

: New Jersey driver

: New York driver

Let be a binary variable indicating whether or not I observe dangerous driving behavior. Based on my entirely subjective experience, I would say the in terms of likelihoods,

so the maximum likelihood (ML) rule would suggest that the driver is from New York.

However, if I take into account my (also entirely subjective) priors on the fraction of drivers from New Jersey and New York, respectively, I would have to say

so the maximum a-posteriori probability (MAP) rule would suggest that the driver is from New Jersey.

Which is better?

[*] I am assuming North Jersey here, so Pennsylvania plates are negligible.

[**] This may be a questionable modeling assumption given suburban demographics.

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I prefer my okra less slimy, but to each their own.

Via Erin, A tour of the old homes of the Mission.

Also via Erin, Women and Crosswords and Autofill.

A statistician rails against computer science’s intellectual practices.

Nobel Laureate Randy Schekman is boycotting *Nature*, *Science*, and *Cell*. Retraction Watch is skeptical.

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**Group Testing with Unreliable Elements**

*Arya Mazumdar, Soheil Mohajer*

This was a generalization of the group testing problem: items are either positive or null, and can be tested in groups such that if any element of the group is positive, the whole group will test positive. The item states can be thought of as a binary column vector and each test is the row or a matrix : the -the entry of is 1 if the -th item is part of the -th group. The multiplication is taken using Boolean OR. The twist in this paper is that they consider a situation where elements can “pretend” to be positive in each test, with a possibly different group in each test. This is different than “noisy group testing” which was considered previously, which is more like . They show achievable rates for detecting the positives using random coding methods (i.e. a random matrix). There was some stuff at the end about the non-i.i.d. case but my notes were sketchy at that point.

**The Minimal Realization Problems for Hidden Markov Models**

*Qingqing Huang, Munther Dahleh, Rong Ge, Sham Kakade*

The realization problem for an HMM is this: given the exact joint probability distribution on length strings from an HMM, can we create a multilinear system whose outputs have the same joint distribution? A multilinear system looks like this:

for with

We think of as the transition for state (e.g. a stochastic matrix) and we want the output to be . This is sort of at the nexus of control/Markov chains and HMMs and uses some of the tensor ideas that are getting hot in machine learning. As the abstract puts it, the results are that they can efficiently construct realizations if , where is the size of the output alphabet size, and is the minimal order of the realization.”

**Differentially Private Distributed Protocol for Electric Vehicle Charging**

*Shuo Han, Ufuk Topcu, George Pappas*

This paper was about a central aggregator trying to get multiple electric vehicle users to report their charging needs. The central allocator has a utility maximization problem over the rates of charging:

They focus on the case where the charge demands are private but the charging rates can be public. This is in contrast to the mechanism design literature popularized by Aaron Roth and collaborators. They do an analysis of a projected gradient descent procedure with Laplace noise added for differential privacy. It’s a stochastic gradient method but seems to converge in just a few time steps — clearly the number of iterations may impact the privacy parameter, but for the examples they showed it was only a handful of time steps.

**An Interactive Information Odometer and Applications**

*Mark Braverman and Omri Weinstein*

This was a communication complexity talk about trying to maintain an estimate of the information complexity of a protocol for computing a function interactively, where Alice has , Bob has , and :

Previous work has shown that the (normalized) communication complexity of computing on -tuples of variables -accurately approaches the minimum information complexity over all protocols for computing once -accurately. At least I think this is what was shown previously — it’s not quite my area. The result in this paper is a strong converse for this — the goal is to maintain an online estimate of during the protocol. The mechanism seeems a bit like communication with feedback a la Horstein, but I got a bit confused as to what was going on — basically it seems that Alice and Bob need to be able to agree on the estimate during the protocol and use a few extra bits added to the communication to maintain this estimate. If I had an extra hours in the week I would read up more about this. Maybe on my next plane ride…

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“In the battle of ideas for metaphors for explaining these phenomena, graphs are doing pretty well for themselves.” — Jon Kleinberg (at the plenary).

Greetings from Allerton! I know blogging has been light since the semester started. I chalk it up to the whole “starting as an assistant professor is time-consuming” thing. I really hope there isn’t a strong converse because I definitely feel like I am operating above capacity.

Regardless, posts to continue again soon. There have been lots of interesting talks here, and lots to follow up on, time permitting.

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**Figure:** *[rapping on the door, in a Highland accent]* Knock knock!

**Voice from inside:** Who’s there?

**Figure:** Glivenko!

**Voice:** Glivenko who?

**Figure:** Glivenko-Cantelli!

*[The fog along the moor converges uniformly on the house, enveloping it completely in a cumulus.]*

*Scene.*

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IEEE Signal Processing Society

IEEE Journal of Selected Topics in Signal Processing

Special Issue on Signal and Information Processing for Privacy

**Aims and Scope**: There has been a remarkable increase in the usage of communications and information technology over the past decade. Currently, in the backend and in the cloud, reside electronic repositories that contain an enormous amount of information and data associated with the world around us. These repositories include databases for data-mining, census, social networking, medical records, etc. It is easy to forecast that our society will become increasingly reliant on applications built upon these data repositories. Unfortunately, the rate of technological advancement associated with building applications that produce and use such data has significantly outpaced the development of mechanisms that ensure the privacy of such data and the systems that process it. As a society we are currently witnessing many privacy-related concerns that have resulted from these technologies—there are now grave concerns about our communications being wiretapped, about our SSL/TLS connections being compromised, about our personal data being shared with entities we have no relationship with, etc. The problems of information exchange, interaction, and access lend themselves to fundamental information processing abstractions and theoretical analysis. The tools of rate-distortion theory, distributed compression algorithms, distributed storage codes, machine learning for feature identification and suppression, and compressive sensing and sampling theory are fundamental and can be applied to precisely formulate and quantify the tradeoff between utility and privacy in a variety of domains. Thus, while rate-distortion theory and information-theoretic privacy can provide fundamental bounds on privacy leakage of distributed data systems, the information and signal processing techniques of compressive sensing, machine learning, and graphical models are the key ingredients necessary to achieve these performance limits in a variety of applications involving streaming data, distributed data storage (cloud), and interactive data applications across a number of platforms. This special issue seeks to provide a venue for ongoing research in information and signal processing for applications where privacy concerns are paramount.

Topics of Interest include (but are not limited to):

Signal processing for information-theoretic privacy

Signal processing techniques for access control with privacy guarantees in distributed storage systems

Distributed inference and estimation with privacy guarantees

Location privacy and obfuscation of mobile device positioning

Interplay of privacy and other information processing tasks

Formalized models for adversaries and threats in applications where consumer and producer privacy is a major concern

Techniques to achieve covert or stealthy communication in support of private communications

Competitive privacy and game theoretic formulations of privacy and obfuscation

**Important Dates:**

Manuscript submission due: October 1, 2014

First review completed: December 15, 2014

Revised manuscript due: February 1, 2015

Second review completed: March 15, 2015

Final manuscript due: May 1, 2015

Publication date: October 2015

Prospective authors should visit the JSTSP website for information on paper submission. Manuscripts should be submitted using Manuscript Central.

Wade Trappe

Rutgers University, USA

trappe@winlab.rutgers.edu

Heejo Lee

Korea University, Korea

heejo@korea.ac.kr

Lalitha Sankar

Arizona State University, USA

lalithasankar@asu.edu

Srdjan Capkun

ETH‐Zurich

srdjan.capkun@inf.ethz.ch

Radha Poovendran

University of Washington, USA

rp3@u.washington.edu

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