Tag Archives: networks

CFP: IEEE JSTSP and T-SIPN Special Issues on Graph Signal Processing

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IEEE Journal of Selected Topics in Signal Processing
IEEE Transactions on Signal and Information Processing over Networks
Special Issues on Graph Signal Processing

Numerous applications rely on the processing of high-dimensional data that resides on irregular or otherwise unordered structures which are naturally modeled as networks (such as social, economic, energy, transportation, telecommunication, sensor, and neural, to name a few). The need for new tools to process such data has led to the emergence of the field of graph signal processing, which merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process signals on structures such as graphs. This important new paradigm in signal processing research, coupled with its numerous applications in very different domains, has fueled the rapid development of an inter-disciplinary research community that has been working on theoretical aspects of graph signal processing and applications to diverse problems such as big data analysis, coding and compression of 3D point clouds, biological data processing, and brain network analysis.

The purpose of these special issues is to gather the latest advances in graph signal processing and disseminate new ideas and experiences in this emerging field to a broad audience. We encourage the submission of papers with new results, methods or applications in graph signal processing. In particular, the topics of interest include (but are not limited to):

  • Sampling and recovery of graph signals
  • Graph filter and filter bank design
  • Uncertainty principles and other fundamental limits
  • Graph signal transforms
  • Graph topology inference
  • Prediction and learning in graphs
  • Statistical graph signal processing
  • Non-linear graph signal processing
  • Applications to visual information processing
  • Applications to neuroscience and other medical fields
  • Applications to economics and social networks
  • Applications to various infrastructure networks

Submission Procedure:
Prospective authors should follow the instructions given on the IEEE JSTSP webpages and submit their manuscript with the web submission system at https://mc.manuscriptcentral.com/jstsp-ieee. The decisions on whether the accepted papers will be published in IEEE JSTSP or IEEE TSIPN will depend on the respective themes of the papers and will be made by the Guest Editors.

Schedule (all deadlines are firm):

Manuscript due: Nov 1, 2016
First Review Completed: Jan 1, 2017
Revised manuscript due: Mar 1, 2017
Second Review Completed: May 1, 2017
Final manuscript due: June 1, 2017
Publication date: September 2017

Guest Editors:

  • Pier-Luigi Dragotti, Imperial College, London (p.dragotti@imperial.ac.uk)
  • Pascal Frossard, EPFL, Lausanne (pascal.frossard@epfl.ch)
  • Antonio Ortega, USC, Los Angeles (ortega@sipi.usc.edu)
  • Michael Rabbat, McGill University, Montreal (michael.rabbat@mcgill.ca)
  • Alejandro Ribeiro, UPenn, Philadelphia (aribeiro@seas.upenn.edu)

CFP: IEEE T-SIPN Special Issue on Distributed Information Processing in Social Networks

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IEEE Signal Processing Society
IEEE Transactions on Signal and Information Processing over Networks
Special Issue on Distributed Information Processing in Social Networks

Over the past few decades, online social networks such as Facebook and Twitter have significantly changed the way people communicate and share information with each other. The opinion and behavior of each individual are heavily influenced through interacting with others. These local interactions lead to many interesting collective phenomena such as herding, consensus, and rumor spreading. At the same time, there is always the danger of mob mentality of following crowds, celebrities, or gurus who might provide misleading or even malicious information. Many efforts have been devoted to investigating the collective behavior in the context of various network topologies and the robustness of social networks in the presence of malicious threats. On the other hand, activities in social networks (clicks, searches, transactions, posts, and tweets) generate a massive amount of decentralized data, which is not only big in size but also complex in terms of its structure. Processing these data requires significant advances in accurate mathematical modeling and computationally efficient algorithm design. Many modern technological systems such as wireless sensor and robot networks are virtually the same as social networks in the sense that the nodes in both networks carry disparate information and communicate with constraints. Thus, investigating social networks will bring insightful principles on the system and algorithmic designs of many engineering networks. An example of such is the implementation of consensus algorithms for coordination and control in robot networks. Additionally, more and more research projects nowadays are data-driven. Social networks are natural sources of massive and diverse big data, which present unique opportunities and challenges to further develop theoretical data processing toolsets and investigate novel applications. This special issue aims to focus on addressing distributed information (signal, data, etc.) processing problems in social networks and also invites submissions from all other related disciplines to present comprehensive and diverse perspectives. Topics of interest include, but are not limited to:

  • Dynamic social networks: time varying network topology, edge weights, etc.
  • Social learning, distributed decision-making, estimation, and filtering
  • Consensus and coordination in multi-agent networks
  • Modeling and inference for information diffusion and rumor spreading
  • Multi-layered social networks where social interactions take place at different scales or modalities
  • Resource allocation, optimization, and control in multi-agent networks
  • Modeling and strategic considerations for malicious behavior in networks
  • Social media computing and networking
  • Data mining, machine learning, and statistical inference frameworks and algorithms for handling big data from social networks
  • Data-driven applications: attribution models for marketing and advertising, trend prediction, recommendation systems, crowdsourcing, etc.
  • Other topics associated with social networks: graphical modeling, trust, privacy, engineering applications, etc.

Important Dates:

  • Manuscript submission due: September 15, 2016
  • First review completed: November 1, 2016
  • Revised manuscript due: December 15, 2016
  • Second review completed: February 1, 2017
  • Final manuscript due: March 15, 2017
  • Publication: June 1, 2017

Guest Editors:

Postdoc at Rutgers ECE in Network Science and Statistical Inference

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My colleague Laleh Najafizadeh has a postdoc position at Rutgers!

The NeuroImaging Laboratory at the Department of Electrical and Computer Engineering (ECE) at Rutgers University is seeking a highly motivated Postdoctoral Fellow to work on an exciting interdisciplinary project at the intersection of Neuroscience, Network Science, and Statistical Learning and Inference. The applicant will have a unique opportunity to be involved in both the theoretical and experimental development of the project.
The position is open to candidates with a Ph.D. in Electrical Engineering, Computer Science, Statistics or related areas, who are self-driven, have a strong background in mathematics, and have excellent analytical and communication skills. Prior experience of working with neuroimaging data (any modality) is a plus. The appointment is available immediately and will be for 1 year.

Please email your current CV and contact information for three references to Dr. Laleh Najafizadeh at laleh.najafizadeh@rutgers.edu. Early submission is strongly encouraged.

The Rutgers ECE NeuroImaging Laboratory is designed to accommodate both single-subject and hyperscanning multi-modal functional neuroimaging experiments, and is equipped with high- resolution EEG and optical imaging (fNIRS) systems. More information about the laboratory can be found at the lab homepage.

The laboratory is located in Rutgers University–New Brunswick, which is situated at the center of the Northeast Corridor, within 20 miles of Princeton, 40 miles of New York City and 70 miles of Philadelphia.

There exist several opportunities to collaborate with clinicians at Rutgers University. Rutgers Biomedical and Health Sciences is home to the Center for Advanced Biotechnology and Medicine as well as Rutgers School of Public Health. The Robert Wood Johnson University Hospital, the flagship hospital of Robert Wood Johnson Health System, is also located few miles from the ECE Department.

Rutgers is an Equal Opportunity / Affirmative Action Employer.

Call for Papers: T-SIPN Special Issue on Inference and Learning Over Networks

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IEEE Signal Processing Society
IEEE Transactions on Signal and Information Processing over Networks
Special Issue on Inference and Learning Over Networks

Networks are everywhere. They surround us at different levels and scales, whether we are dealing with communications networks, power grids, biological colonies, social networks, sensor networks, or distributed Big Data depositories. Therefore, it is not hard to appreciate the ongoing and steady progression of network science, a prolific research field spreading across many theoretical as well as applicative domains. Regardless of the particular context, the very essence of a network resides in the interaction among its individual constituents, and Nature itself offers beautiful paradigms thereof. Many biological networks and animal groups owe their sophistication to fairly structured patterns of cooperation, which are vital to their successful operation. While each individual agent is not capable of sophisticated behavior on its own, the combined interplay among simpler units and the distributed processing of dispersed pieces of information, enable the agents to solve complex tasks and enhance dramatically their performance. Self-organization, cooperation and adaptation emerge as the essential, combined attributes of a network tasked with distributed information processing, optimization, and inference. Such a network is conveniently described as an ensemble of spatially dispersed (possibly moving) agents, linked together through a (possibly time – varying) connection topology. The agents are allowed to interact locally and to perform in-network processing, in order to accomplish the assigned inferential task. Correspondingly, several problems such as, e.g., network intrusion, community detection, and disease outbreak inference, can be conveniently described by signals on graphs, where the graph typically accounts for the topology of the underlying space and we obtain multivariate observations associated with nodes/edges of the graph. The goal in these problems is to identify/infer/learn patterns of interest, including anomalies, outliers, and existence of latent communities. Unveiling the fundamental principles that govern distributed inference and learning over networks has been the common scope across a variety of disciplines, such as signal processing, machine learning, optimization, control, statistics, physics, economics, biology, computer, and social sciences. In the realm of signal processing, many new challenges have emerged, which stimulate research efforts toward delivering the theories and algorithms necessary to (a) designing networks with sophisticated inferential and learning abilities; (b) promoting truly distributed implementations, endowed with real-time adaptation abilities, needed to face the dynamical scenarios wherein real-world networks operate; and (c) discovering and disclosing significant relationships possibly hidden in the data collected from across networked systems and entities. This call for papers therefore encourages submissions from a broad range of experts that study such fundamental questions, including but not limited to:

  • Adaptation and learning over networks.
  • Consensus strategies; diffusion strategies.
  • Distributed detection, estimation and filtering over networks.
  • Distributed dictionary learning.
  • Distributed game-theoretic learning.
  • Distributed machine learning; online learning.
  • Distributed optimization; stochastic approximation.
  • Distributed proximal techniques, sub-gradient techniques.
  • Learning over graphs; network tomography.
  • Multi-agent coordination and processing over networks.
  • Signal processing for biological, economic, and social networks.
  • Signal processing over graphs.

Prospective authors should visit http://www.signalprocessingsociety.org/publications/periodicals/tsipn/ for information on paper submission. Manuscripts should be submitted via Manuscript Central at http://mc.manuscriptcentral.com/tsipn-ieee.

Important Dates:

  • Manuscript submission: February 1, 2016
  • First review completed: April 1, 2016
  • Revised manuscript due: May 15, 2016
  • Second review completed: July 15, 2016
  • Final manuscript due: September 15, 2016
  • Publication: December 1, 2016

Guest Editors:

 

Call for Papers: T-SIPN Special Issue on Distributed Information Processing in Social Networks

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IEEE Signal Processing Society
IEEE Transactions on Signal and Information Processing over Networks
Special Issue on Distributed Information Processing in Social Networks

Over the past few decades, online social networks such as Facebook and Twitter have significantly changed the way people communicate and share information with each other. The opinion and behavior of each individual are heavily influenced through interacting with others. These local interactions lead to many interesting collective phenomena such as herding, consensus, and rumor spreading. At the same time, there is always the danger of mob mentality of following crowds, celebrities, or gurus who might provide misleading or even malicious information. Many efforts have been devoted to investigating the collective behavior in the context of various network topologies and the robustness of social networks in the presence of malicious threats. On the other hand, activities in social networks (clicks, searches, transactions, posts, and tweets) generate a massive amount of decentralized data, which is not only big in size but also complex in terms of its structure. Processing these data requires significant advances in accurate mathematical modeling and computationally efficient algorithm design. Many modern technological systems such as wireless sensor and robot networks are virtually the same as social networks in the sense that the nodes in both networks carry disparate information and communicate with constraints. Thus, investigating social networks will bring insightful principles on the system and algorithmic designs of many engineering networks. An example of such is the implementation of consensus algorithms for coordination and control in robot networks. Additionally, more and more research projects nowadays are data-driven. Social networks are natural sources of massive and diverse big data, which present unique opportunities and challenges to further develop theoretical data processing toolsets and investigate novel applications. This special issue aims to focus on addressing distributed information (signal, data, etc.) processing problems in social networks and also invites submissions from all other related disciplines to present comprehensive and diverse perspectives. Topics of interest include, but are not limited to:

  • Dynamic social networks: time varying network topology, edge weights, etc.
  • Social learning, distributed decision-making, estimation, and filtering
  • Consensus and coordination in multi-agent networks
  • Modeling and inference for information diffusion and rumor spreading
  • Multi-layered social networks where social interactions take place at different scales or modalities
  • Resource allocation, optimization, and control in multi-agent networks
  • Modeling and strategic considerations for malicious behavior in networks
  • Social media computing and networking
  • Data mining, machine learning, and statistical inference frameworks and algorithms for handling big data from social networks
  • Data-driven applications: attribution models for marketing and advertising, trend prediction, recommendation systems, crowdsourcing, etc.
  • Other topics associated with social networks: graphical modeling, trust, privacy, engineering applications, etc.

Important Dates:

Manuscript submission due: September 15, 2016
First review completed: November 1, 2016
Revised manuscript due: December 15, 2016
Second review completed: February 1, 2017
Final manuscript due: March 15, 2017
Publication: June 1, 2017

Guest Editors:

Zhenliang Zhang, Qualcomm Corporate R&D (zhenlian@qti.qualcomm.com)
Wee Peng Tay, Nanyang Technological University (wptay@ntu.edu.sg)
Moez Draief, Imperial College London (m.draief@imperial.ac.uk)
Xiaodong Wang, Columbia University (xw2008@columbia.edu)
Edwin K. P. Chong, Colorado State University (edwin.chong@colostate.edu)
Alfred O. Hero III, University of Michigan (hero@eecs.umich.edu)

Postdoctoral position at University of Michigan

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A postdoctoral position is available at the University of Michigan Electrical Engineering and Computer Science Department for a project related to anomaly detection in networked cyber-physical systems. The successful applicant will have knowledge in one or more of the following topics: convex optimization and relaxations, compressed sensing, distributed optimization, submodularity, control and dynamical systems or system identification. The project will cover both theory and algorithm development and some practical applications in fault and attack detection in transportation and energy networks. The position can start anytime in 2014 or early 2015. This is a one year position, renewable for a second year. Interested candidates should contact Necmiye Ozay at necmiye@umich.edu with a CV and some pointers to representative publications.

“Cascading Style Sheets are a cryptic language developed by the Freemasons to obscure the visual nature of reality”

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Via Cynthia, here is a column by James Mickens about how horrible the web is right now:

Computer scientists often look at Web pages in the same way that my friend looked at farms. People think that Web browsers are elegant computation platforms, and Web pages are light, fluffy things that you can edit in Notepad as you trade ironic comments with your friends in the coffee shop. Nothing could be further from the truth. A modern Web page is a catastrophe. It’s like a scene from one of those apocalyptic medieval paintings that depicts what would happen if Galactus arrived: people are tumbling into fiery crevasses and lamenting various lamentable things and hanging from playground equipment that would not pass OSHA safety checks.

It’s a fun read, but also a sentiment that may echo with those who truly believe in “clean slate networking.” I remember going to a tutorial on LTE and having a vision of what 6G systems will look like. One thing that is not present, though, is the sense that the system is unstable, and that the introduction of another feature in communication systems will cause the house of cards to collapse. Mickens seems to think the web is nearly there. The reason I thought of this is the recent fracas over the US ceding control of ICANN, and the sort of doomsdaying around that. From my perspective, network operators are sufficiently conservative that they can’t/won’t willy-nilly introduce new features that are only half-supported, like the in Web. The result is a (relatively) stable networking world that appears to detractors as somewhat Jurassic.

I’d argue (with less hyperbole) that some of our curriculum ideas also suffer from the accretion of old ideas. When I took DSP oh-so-long ago (13 years, really?) we learned all of this Direct Form Transposed II blah blah which I’m sure was useful for DSP engineers at TI to know at some point, but has no place in a curriculum now. And yet I imagine there are many places that still teaching it. If anyone reads this still, what are the dinosaurs in your curriculum?

Postdoc at INRIA-ENS Paris on Graphs, Algorithms and Probability

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Applications are invited for a Postdoc position (full-time, up to 2 years) at INRIA-ENS in Paris. The position is funded by the ANR GAP grant “Graphs, Algorithms and Probability.”

Requirements are a PhD degree in Computer Science or Mathematics and a strong background in some of the following topics:

  • discrete probability
  • statistical learning
  • combinatorial optimization
  • stochastic networks

Applications must include a research statement, a CV and the names and contacts of references. All material should be sent by email to Marc Lelarge. Please indicate in the subject POSTDOC GAP.

Important dates:

  • Intention of application (short email): as soon as possible
  • Deadline for application: December 1st, 2013
  • Suggested starting dates: Jan.-Feb. 2014

ISIT Blogging, part 3

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I’ll round out the end of my ISIT blogging with very brief takes on a few more papers. I took it pretty casually this year in terms of note taking, and while I attended many more talks, my notes for most of them consist of a title and a star next to the ones where I want to look at the paper more closely. That’s probably closer to how most people attend conferences, only they probably use the proceedings book. I actually ended up shredding the large book of abstracts to use as bedding for my vermicompost (I figured they might appreciate eating a little Turkish paper for a change of diet).

On Connectivity Thresholds in Superposition of Random Key Graphs on Random Geometric Graphs
B Santhana Krishnan (Indian Institute of Technology, Bombay, India); Ayalvadi Ganesh (University of Bristol, United Kingdom); D. Manjunath (IIT Bombay, India)
This looked at a model where you have a random geometric graph (RGG) together with a uniformly chosen random subset S_i of \{ 1, 2, \ldots, P_n\} of size K_n at each node. The subset is the set of keys available at each node; two nodes can talk (securely) if they share a key in common. We keep the edge in the RGG is if the link can be secured. The question is whether the secure-link graph is connected. It turns out that the important scaling is in terms of r_n^2 K_n^2/P_n, where r_n is the connectivity radius of the RGG. This sort of makes sense, as the threshold is more or less \Theta(\log n/n), so the keys provide a kind of discount factor on effective radius needed for connectivity — if the number of keys per node is small then you need a larger radius to compensate.

Secure Network Coding for Distributed Secret Sharing with Low Communication Cost
Nihar B Shah (University of California at Berkeley, USA); K. v. Rashmi (University of California at Berkeley, USA); Kannan Ramchandran (University of California at Berkeley, USA)
This paper was on secret sharing — a dealer wants to distribute n shares of a secret such that any k of them can be used to reconstruct the secret but k-1 or fewer cannot. The idea here is that the dealer has to distribute these shares over the network, which means that if a receiver is not connected directly to the dealer then the share will be passed insecurely through another node. Existing approaches based on pairwise agreement protocols are communication intensive. The idea here is use ideas from network coding to share masked versions of shares so that intermediate nodes will not get valid shares from others. To do this the graph needs to satisfy a particular condition (k-propagating), which is defined in the paper. A neat take on the problem, and worth looking at if you’re interested in that sort of thing.

Conditional Equivalence of Random Systems and Indistinguishability Proofs
Ueli Maurer (ETH Zurich, Switzerland)
This was scheduled to be in the same session as my paper with Vinod, but was moved to an earlier session. Maurer’s “programme” as it were, is to think about security via three kinds of systems — real systems with real protocols and pseudorandomness, idealized systems with real protocols but real randomness, and perfect systems which just exist on paper. The first two are trivially indistinguishable from a computational perspective, and the goal is to show that the last two are information-theoretically indistinguishable. This conceptual framework is actually useful for me to separate out the CS and IT sides of the security design question. This paper tried to set up a framework in which there is a distinguisher D which tries to make queries to two systems and based on the answers has to decide if they are different or not. I think if you’re interested in sort of a systems-theoretic take on security you should take a look at this.

Tight Bounds for Universal Compression of Large Alphabets
Jayadev Acharya (University of California, San Diego, USA); Hirakendu Das (University of California San Diego, USA); Ashkan Jafarpour (UCSD, USA); Alon Orlitsky (University of California, San Diego, USA); Ananda Theertha Suresh (University of California, San Diego, USA)
The main contribution of this paper was to derive bounds on compression of patterns of sequences over unknown/large alphabets. The main result is that the worst case pattern redundancy for i.i.d. distributions is basically n^{1/3} where n is the blocklength. The main result is a new upper bound which uses some tricks like sampling a random number of points, where the number of samples is Poisson distributed, and a partition of the set of distributions induced by Poisson sampling.

To Surprise and Inform
Lav R. Varshney (IBM Thomas J. Watson Research Center, USA)
Lav talked about communication over a channel where the goal is to communicate subject to a constraint on the Bayesian surprise s(x) = D( p(Y|x) \| P(Y) ) where X and Y are the input and output of the channel. He gets a single-letter expression for the capacity under a bound on the max surprise and gives an example for which the same distribuion maximizes mutual information and achieves the minimax surprise. The flip side is to ask for capacity when each output should be surprising (or “attention seeking”). He gets a single letter capacity here as well, but the structure of the solution seems to be a bit more complicated.

ISIT Blogging, part 2

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Logarithmic Sobolev inequalities and strong data processing theorems for discrete channels
Maxim Raginsky (University of Illinois at Urbana-Champaign, USA)
Max talked about how the strong data processing inequality (DPI) is basically a log-Sobolev inequality (LSI) that is used in measure concentration. The strong DPI says that
D(QW \| PW) \le \alpha D(Q \| P)
for some \alpha < 1, so the idea is to get bounds on
\delta^*(P,W) = \sup_{Q} \frac{D(QW \| PW)}{D(Q \| P)}.
What he does is construct a hierarchy of LSIs in which the strong DPI fits and then gets bounds on this ratio in terms of best constants for LSIs. The details are a bit hairy, and besides, Max has his own blog so he can write more about it if he wants…

Building Consensus via Iterative Voting
Farzad Farnoud (University of Illinois, Urbana-Champaign, USA); Eitan Yaakobi (Caltech, USA); Behrouz Touri (University of Illinois Urbana-Champaign, USA); Olgica Milenkovic (University of Illinois, USA); Jehoshua Bruck (California Institute of Technology, USA)
This paper was about rank aggregation, or how to take a bunch of votes expressed as permutations/rankings of options to produce a final option. The model is one in which people iteratively change their ranking based on the current ranking. For example, one could construct the pairwise comparison graph (a la Condorcet) and then have people change their rankings when they disagree with the majority on an edge. They show conditions under which this process converges (the graph should not have a cycle) and show that if there is a Condorcet winner, then after this process everyone will rank the Condorcet winner first. They also look at a Borda count version of this problem but to my eye that just looked like an average consensus method, but it was at the end of the talk so I might have missed something.

Information-Theoretic Study of Voting Systems
Eitan Yaakobi (Caltech, USA); Michael Langberg (Open University of Israel, Israel); Jehoshua Bruck (California Institute of Technology, USA)
Eitan gave this talk and the preceding talk. This one was about looking at voting through the lens of coding theory. The main issue is this — what sets of votes or distribution of vote profiles will lead to a Condorcet winner? Given a set of votes, one could look at the fraction of candidates who rank candidate j in the i-th position and then try to compute entropies of the resulting distributions. The idea is somehow to characterize the existence or lack of a Condorcet winner in terms of distances (Kendall tau) and these entropy measures. This is different than looking at probability distributions on permutations and asking about the probability of there existing a Condorcet cycle.

Brute force searching, the typical set and Guesswork
Mark Chirstiansen (National University of Ireland Maynooth, Ireland); Ken R Duffy (National University of Ireland Maynooth, Ireland); Flávio du Pin Calmon (Massachusetts Institute of Technology, USA); Muriel Médard (MIT, USA)
Suppose an item X is chosen \sim P from a list and we want to guess the choice that is made. We’re only allowed to ask questions of the form “is the item Y?” Suppose now that the list is a list of codewords of blocklength k drawn i.i.d. according to Q. This paper looks at the number of guesses one needs if P is uniform on the typical set according to Q versus when P is distributed according the distribution Q^k conditioned on X being in the typical set. The non-uniformity of the latter turns out to make the guessing problem a lot easier.

Rumor Source Detection under Probabilistic Sampling
Nikhil Karamchandani (University of California Los Angeles, USA); Massimo Franceschetti (University of California at San Diego, USA)
This paper looked at an SI model of infection on a graph — nodes are either Susceptible (S) or Infected (I), and there is a probability of transitioning from S to I based on your neighbors’ states. There in exponential waiting time \tau_{ij} for the i to infect j if i is infected. The idea is that the rumor starts somewhere and infects a bunch of people and then you get to observe/measure the network. You want to find the source. This was studied by Zaman and Shah under the assumption of perfect observation of all nodes. This work looked at the case where nodes randomly report their infection state, so you only get an incomplete picture of the infection state. They characterize the effect of the reporting probability on the excess error and show that for certain tree graphs, incomplete reporting is as good as full reporting.