# Student Promotion: Signal Processing Society Provides Steep Price Slash

Or SPSPSPSPS, for short. I’ve been over-busy and lax on posting, but I’ll provide some recap of ITA soon, as well as some notes from the Bellairs workshop I just came back from. The winter is a bit jarring. To the point of the subject:

In case you hadn’t heard, the IEEE Signal Processing Society is currently running a campaign that allows IEEE Student and Graduate Student members to join the SPS for free for the 2015 membership year. The promotion is running now through 15 August 2015. Only IEEE Student and Graduate Students are eligible, as this offer does not apply to SPS Student or Graduate Student members renewing their membership for 2015.

This link directs to the IEEE website with both IEEE Student membership and the free SPS Student membership in the cart.

If a student is already an IEEE Student of Graduate Student member, he/she can use the code SP15STUAD at checkout to obtain his/her free membership.

If you have any questions regarding the SPS Free Student Membership campaign or other membership items, please don’t hesitate to contact Jessica Perry at jessica.perry@ieee.org.

# WIFS 2014

This week I took a quick jaunt down to Atlanta to attend part of WIFS 2014 (co-located with GlobalSIP 2014). Kamalika and I were invited to give a talk on differential privacy and machine learning, based on our IEEE Signal Processing Magazine article. I’ve uploaded the slides of the tutorial to my website and we’re planning on making a video (audio over slides) version for SigView as well as on YouTube.

Much like last year, GlobalSIP had a somewhat disjointed, semi-chaotic feel (exacerbated by tiredness, I am sure) — it’s really a collection of semi-interacting workshops in the same space, and I knew people in several of the other workshops. Since I was there for a day and giving a tutorial at WIFS, I decided to stick with WIFS for the day. To give a sense of how confusing it all was, here’s a picture of the guide to deciphering the program book:

Overly-complicated rules for encoding sessions

The keynote for GlobalSIP was given by Vince Poor on information-theoretic privacy via rate distortion (this is the work with Lalitha). Vince did a good job of not over-IT-ing it I think, which was good because the audience was pretty diverse and it’s not clear that many of the people there had even taken a course on information theory. This seems to be the big challenge in multi-disciplinary conferences like GlobalSIP (or large signal processing conferences in general) — everyone is in signal processing, but it’s a big tent and it’s hard to reach everyone.

Min Wu was the keynote speaker for the WIFS workshop on the day I attended. Her talk, on “Exploring Power Network Signatures for Information Forensics” was about how to glean information from power fluctuations in networks, or electronic network frequency (ENF). Different processes or operations have different power demands — by matching these signatures to an observed signal (e.g. a video), one can make inferences about the time/location/integrity of the data. For example, were the audio and visual tracks in a video taken at the same time or merged later? This whole area is quite interesting, and while I was sort of aware of this work I hadn’t really read up on much of it.

Perhaps it was the end of the semester kicking in, but I sort of took terrible notes on most of the talks and poster sessions at the conference, so I can’t really write coherently about the papers I saw. Unfortunately I had to run back to teach the penultimate lecture in my class. I guess now that I have a “real job” this is going to be the way it works from now on. Kind of sad, really.

# Transactions on Signal and Information Processing Over Networks: now accepting papers

I’m an Associate Editor for the new IEEE Transactions on Signal and Information Processing Over Networks, and we are accepting submissions now. The Editor-In-Chief is Petar M. Djurić.

The new IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data.

To submit a paper, go to Manuscript Central.

Topics of interest include, but are not limited to the following:

• Distributed detection and estimation
• Distributed learning over networks
• Distributed target tracking
• Bayesian learning; Bayesian signal processing
• Sequential learning over networks
• Decision making over networks
• Distributed dictionary learning
• Distributed game theoretic strategies
• Distributed information processing
• Graphical and kernel methods
• Consensus over network systems
• Optimization over network systems

Communications, Networking, and Sensing

• Distributed monitoring and sensing
• Signal processing for distributed communications and networking
• Signal processing for cooperative networking
• Signal processing for network security
• Optimal network signal processing and resource allocation

Modeling and Analysis

• Performance and bounds of methods
• Robustness and vulnerability
• Network modeling and identification
• Simulations of networked information processing systems
• Social learning
• Bio-inspired network signal processing
• Epidemics and diffusion in populations

Imaging and Media Applications

• Image and video processing over networks
• Media cloud computing and communication
• Multimedia streaming and transport
• Social media computing and networking
• Signal processing for cyber-physicalsystems
• Wireless/mobile multimedia

Data Analysis

• Processing, analysis, and visualization of big data
• Signal and information processing for crowd computing
• Signal and information processing for the Internet of Things
• Emergence of behavior

Emerging topics and applications

• Emerging topics
• Applications in life sciences, ecology, energy, social networks, economic networks, finance, social sciences, smart grids, wireless health, robotics, transportation, and other areas of science and engineering

# Detection and Estimation: book recommendations?

It’s confirmed that I will be teaching Detection and Estimation next semester so I figured I would use the blog to conjure up some book recommendations (or even debate, if I can be so hopeful). Some of the contenders:

• 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.

# Towards multi-sensor characterizations of pianos

As an undergraduate I became interested in how timbre can be used to identify musical instruments. This was largely due to my first UROP (undergraduate research gig) with Keith Martin at the MIT Media Lab. Keith’s thesis was on identifying musical instruments from spectral features, and I worked a bit on this under Ryan Rifkin in a later UROP. I’ve been catching up on podcasts during my commute to campus this week, and a semi-recent Science Friday piece on the Steinway factory was on deck for this morning.

The piece talks about work in Agnieszka Roginska‘s lab at NYU, and in particular work from a paper from last year on measuring radiation patterns in piano soundboards. The radiation patterns are pretty but a bit hard to interpret, largely because I’m way out of the acoustical signal processing world. However, what’s interesting to me is that we’re still largely focused on overtones/cepstral coefficients. I wonder about how one might discover more interesting features to characterize this data. (I know someone will suggest deep learning but color me a little skeptical).

As a side note, one of the recent popular articles from JASA is on the acoustics of coffee roasting.

# SPCOM 2014: some more talks (and a plenary)

I did catch Greg Wornell’s plenary at SPCOM, which was called When Bits Absolutely, Positively, Have to be There as Soon as Possible, a riff on this FedEx commercial, which is older than I am. The talk was on link-aware PHY-layer design– basically looking at how ARQ enables incremental redundancy, and how to do a sort of layered superposition + incremental redundancy scheme in the sequential setting as well as a “multi-path” setting where blocks can arrive out of order. This was really digging into the signal issues in a way that a lot of non-communication engineering information theorists may get squeamish about. The nice thing is that I think the engineering problem is approachable without knowing a lot of heavy-duty math, but still requires some careful analysis.

Communication and Compression Via Sparse Linear Regression
Ramji Venkataramanan
This was on building codewords and codebooks out of a lower-complexity code dictionary $A \in \mathbb{R}^{n \times ML}$ where each codeword is a superposition of $L$ columns, one each from groups of size $M$. Thus encoding is $A \beta$ where $\beta$ is a sparse vector. I saw a talk by Barron and Joseph from a previous ISIT about this, but the framework extends to rate distortion (achieving the rate distortion function), and channel coding. The main point is to lower the complexity of the code at the expense of the gap to optimal rate — encoding and decoding are polynomial time but the rate gap for rate-distortion goes to zero as $1/\log n$. Ramji gave a really nice and clear talk on this — I hope he puts the slides up!

An Optimal Varentropy Bound for Log-Concave Distributions
Mokshay’s talk was also really clear and excellent. For a distribution $f(X)$ on $\mathbb{R}^n$, we can define $\tilde{h}(X) = - \log f(X)$. The entropy is the expectation of this random variable, and the varentropy is the variance. Their main result is a upper bound on the varentropu of log concave distributions $f(X)$. To wit, $\mathrm{Var}(\tilde{h}) \le n$. This bound doesn’t depend on the distribution and is sharp if $f$ is a product of exponentials. They then use this to prove a universal bound on the deviation of $\tilde{h}$ from its expectation, which gives a AEP that doesn’t really assume anything about the joint distribution of the variables except for log-concavity. There was more in the talk, but I eagerly await the paper.

Event-triggered Sampling and Reconstruction of Sparse Real-valued Trigonometric Polynomials
Neeraj Sharma; Thippur V. Sreenivas
This was on non-uniform sampling where the sampler tries to detect level crossings of the analog signal and samples at that point — the rate may not be uniform enough to use existing nonuniform sampling techniques. They come up with a method for reconstructing signals which are real-valued trigonometric polynomials with a few nonzero coefficients (e.g. sparse) and it seems to work pretty decently in experiments.

Removing Sampling Bias in Networked Stochastic Approximation
Vivek Borkar; Raaz Dwivedi
In networked stochastic approximation, the intermittent communication between nodes may mean that the system tracks a different ODE than the one we want. By modifying the method to account for “local clocks” on each edge, we can correct for this, but we end up with new conditions on the step size to make things work. I am pretty excited about this paper, but as usual, my notes were not quite up to getting the juicy bits. That’s what paper reading is for.

On Asymmetric Insertion and Deletion Errors
Ankur A. Kulkarni
The insertion/deletion channel model is notoriously hard. Ankur proposed a new model where $0$‘s are “indestructible” — they cannot be inserted or deleted. This asymmetric model leads to new asymptotic bounds on the capacity. I don’t really work on this channel model so I can’t get the finer points of the results, but once nice takeaway was that asymptotically, each indestructible $0$ in the codeword lets us correct around $1/2$ a deletion more.

# ISIT 2014: how many samples do we need?

Due to jetlag, my CAREER proposal deadline, and perhaps a bit of general laziness, I didn’t take as many notes at ISIT as I would have, so my posting will be somewhat light (in addition to being almost a month delayed). If someone else took notes on some talks and wants to guest-post on it, let me know!

Strong Large Deviations for Composite Hypothesis Testing
Yen-Wei Huang (Microsoft Corporation, USA); Pierre Moulin (University of Illinois at Urbana-Champaign, USA)
This talk was actually given by Vincent Tan since neither of the authors could make it (this seems to be a theme of talks I’ve attended this summer. The paper was about testing a simple hypothesis $H_1$ versus a composite hypothesis $H_0$ where under $H_0$ the observations are i.i.d. with respect to one of possibly $k$ different distributions. There are therefore $k$ different errors and the goal is to characterize these errors when we ask for the probability of true detection to be greater than $1 - \epsilon$. This is a sort of generalized Neyman-Pearson setup. They look at the vector of log-likelihood ratios and show that a threshold test is nearly optimal. At the time, I understood the idea of the proof, but I think it’s one of things where you need to really read the paper.

Randomized Sketches of Convex Programs with Sharp Guarantees
Mert Pilanci (University of California, Berkeley, USA); Martin J. Wainwright (University of California, Berkeley, USA)
This talk was about using random projections to lower the complexity of solving a convex program. Suppose we want to minimize $\| Ax - y \|^2$ over $x$ given $y$. A sketch would be to solve $\| SAx - Sy \|^2$ where $S$ is a random projection. One question is how to choose $A$. They show that choosing $S$ to be a randomized Hadamard matrix (the paper studies Gaussian matrices), then the objective value of the sketched program is at most $(1 + \epsilon)^2$ times the value of the original program as long as the the number of rows of $S$ is larger than $O( \epsilon^{-2} \mathbb{W}^2(A \mathcal{K}))$, where $\mathbb{W}(A \mathcal{K})$ is the Gaussian width of the tangent cone of the contraint set at the optimum value. For more details look at their preprint on ArXiV.

On Efficiency and Low Sample Complexity in Phase Retrieval
Youssef Mroueh (MIT-IIT, USA); Lorenzo Rosasco (DIBRIS, Unige and LCSL – MIT, IIT, USA)
This was another talk not given by the authors. The problem is recovery of a complex vector $x_0 \in \mathbb{C}^n$ from phaseless measurements of the form $b_i = |\langle a_i, x_0 \rangle|^2$ where $a_i$ are complex spherically symmetric Gaussian vectors. Recovery from such measurements is nonconvex and tricky, but an alternating minimizing algorithm can reach a local optimum, and if you start it in a “good” initial position, it will find a global optimum. The contribution of this paper is provide such a smart initialization. The idea is to “pair” the measurements to create new measurements $y_i = \mathrm{sign}( b_i^{(1)} - b_i^{(2)} )$. This leads to a new problem (with half as many measurements) which is still hard, so they find a convex relaxation of that. I had thought briefly about such sensing setups a long time ago (and by thought, I mean puzzled over it at a coffeshop once), so it was interesting to see what was known about the problem.

Sorting with adversarial comparators and application to density estimation
Jayadev Acharya (University of California, San Diego, USA); Ashkan Jafarpour (University of California, San Diego, USA); Alon Orlitsky (University of California, San Diego, USA); Ananda Theertha Suresh (University of California, San Diego, USA)
Ashkan gave this talk on a problem where you have $m$ samples from an unknown distribution $p$ and a set of distributions $\{q_1, q_2, \ldots, q_n\}$ to compare against. You want to find the distribution that is closest in $\ell_1$. One way to do this is via Scheffe tournament tht compares all pairs of distributions — this runs in time $n^2$ time. They show a method that runs in $O(n)$ time by studying the structure of the comparators used in the sorting method. The motivation is that running comparisons can be expensive (especially if they involve human decisions) so we want to minimize the number of comparisons. The paper is significantly different than the talk, but I think it would definitely be interesting to those interested in discrete algorithms. The density estimation problem is really just a motivator — the sorting problem is far more general.