**
Communication, Control**
**
and Signal Processing Seminar****
**

**Abstracts
of talks**

*Fall 2014*

** 10/16 Katie Haymaker (Villanova University)****,
Using a Graph Perspective to Investigate Bit Assignments for Codes
**

ABSTRACT:
This talk will focus on applying (j,k)-regular LDPC codes to a memory setting with two
different bit-error rates. We will discuss how changes in local connectivity to the two types of
bits impacts decoding performance for a particular decoding algorithm. Using these results,
we propose an assignment scheme that approaches the desired degree distributions. We will
conclude by discussing variations of this problem that arise naturally in applications.

** 10/30, 11/6 Prakash Narayan (UMD)****,
Interactive Function Computation **

ABSTRACT:
Information theoretic models for multiuser source and channel coding
usually take the communication between multiple terminals to be ``simple"
or autonomous. On the other hand, studies of multiparty function
computation, especially in computer science, highlight the useful role
of interactive communication. We shall describe basic
structural properties of interactive communication. ``Single-shot"
bounds will be presented for the amount of common randomness, i.e., shared
information, that can be generated among the terminals using such
communication. A few simple consequences with applications will be discussed.
This talk is based on joint works with Imre Csiszár, Sirin Nitinawarat,
Himanshu Tyagi and Shun Watanabe.

** 11/20 Marcos Vasconcelos (UMD)****,
Distributed Estimation over the Collision Channel **

ABSTRACT:
Consider a system consisting of two sensors and a remote estimator connected by
a network modeled by a collision channel. The collision channel is a simplified model for
interference, which can only convey one packet per unit time and declares a collision when
both sensors simultaneously transmit. In this talk, we will formulate a one-shot problem where
the sensors measure independent random variables and must communicate them through the
collision channel to a remote estimator, which is interested in forming an estimate of both
measurements. We will show that the communication policies that minimize a mean squared
error are deterministic threshold policies and that this structure is independent of the probability
distribution of the random variables. We will then show how to explicitly compute the communication
and estimation rules by using a modified version of the Lloyd-Max algorithm. If time permits,
we will prove that this algorithm converges globally to a locally optimal solution when the random
variables are Gaussian.

This is joint work with Prof. Nuno Martins.

** 12/04 Siddharth Pal (UMD)****,
Learning in Games **

ABSTRACT:
We will start with a brief introduction to the language of game theory which will
include discussions on strategic-form repeated games, various notions of equilibria and relevant
convergence notions to such equilibria. Next, a brief overview of relevant learning rules
will be given that are already available in the literature.
The talk will move on to a description of a simple proposed learning rule in games.
We demonstrate that this intuitive rule guarantees almost sure convergence to a pure-strategy
Nash equilibrium in a large class of games we call generalized weakly acyclic games (GWAGs).
We also show that the probability that the action profile does not converge to a pure-strategy
Nash equilibrium decreases geometrically fast in the aforementioned class of games. The next half
of the talk will look at a setting with delays in the game structure. We will look at various forms of
delays and explore if the proposed learning rule can cope with such delays. Finally, if time permits,
we will discuss various refinements of Nash equilibrium that is robust to “faulty” or
“unexpected“ behavior and coalitions among agents.
This talk is based on joint works with Prof. Richard La.

** 12/10 Sunav Choudhary (USC)****,
Identifiability Analysis in Bilinear Inverse Problems with Application to
Sparse Blind Deconvolution **

ABSTRACT:
A number of difficult non-linear inverse problems in signal processing, like blind
deconvolution, matrix factorization, dictionary learning and blind source separation share
the common characteristic of being bilinear inverse problems. A key concern for these inverse
problems in applications like blind equalization in wireless communications and data mining in
machine learning is that of identifiability of the generative model. In this talk, we exploit a
connection to low-rank matrix recovery and develop a flexible and unifying framework for
the analysis of identifiability in cone constrained bilinear inverse problems to derive sufficient
conditions and scaling laws. For blind deconvolution in particular, our approach results in a
measure theoretically tight partially parametric and partially recursive characterization of the
ambiguity space leading to a surprising negative result on the identifiability of canonical-sparse
blind deconvolution.

This is joint work with Prof. Urbashi Mitra at University of Southern California.

** 12/11 Biswadip Dey (UMD)****,
Data Smoothing via Optimal Control **

ABSTRACT:
The problem of recovering continuous time signals from a set of discrete measurements
is ill-posed in a classical sense (high sensitivity to noise, non-uniqueness of solution). However,
this lack of well-posedness can be tackled through a regularized approach. The main idea behind
regularization is to embed the problem of interest into a hypothesis space and minimize a cost
functional expressed as a sum of two terms: (i) misfit of a hypothesis to observed data; and
(ii) a penalty functional accounting for complexity of a hypothesis.
In our context, we build the hypothesis space by introducing generative models governed by ordinary
differential equations with inputs, states and outputs. This yields the hypothesis as output of the
generative model, given an input (control), and we regularize this problem by trading total fit-error
against suitable penalty functionals of input and state. This enables us to apply techniques from optimal
control (e.g. Riccati equation, Pontryagin’s maximum principle) and obtain solutions in a (semi)-analytical
way.

This talk is based on joint works with Prof. P. S. Krishnaprasad.