Research Topics

About wireless stochastic geometry

Stochastic geometry provides a natural way of defining and computing macroscopic properties of communication channels of multi-user information theory. These macroscopic properties are obtained by some averaging over all node patterns found in a large random network of the Euclidean plane or space. 

For more on the field, see the wikipedia page on the matter.

This domain of research is currently expanding exponentially fast as shown by this curve communicated by Martin Haenggi.

Community Detection on Euclidean Random Graphs

In this paper, Abishek Sankararaman and François Baccelli introduce the problem of Community Detection on a new class of sparse spatial random graphs embedded in the Euclidean space. They consider the planted partition version of the random connection model graph studied in classical stochastic geometry. The nodes of the graph form a marked Poisson Point Process of intensity \lambda with each node being equipped with an i.i.d. uniform mark drawn from {-1,+1}. Conditional on the labels, edges are drawn independently at random depending both on the Euclidean distance between the nodes and the community labels on the nodes. The paper studies the Community Detection problem on this random graph which consists in estimating the partition of nodes into communities, based on an observation of the random graph along with the spatial location labels on nodes. For all dimensions greater than or equal to 2, they establish a phase transition in the intensity of the point process. They show that if the intensity is small, then no algorithm for Community Detection can beat a random guess. This is proven by introducing and analyzing a new problem called ‘Information Flow from Infinity’. On the positive side, they give an efficient algorithm to perform Community Detection as long as the intensity is sufficiently high. Along the way, a distinguishability result is established, which says that one can always infer the presence of a partition, even when one cannot identify the partition better than at random. This is a surprising new phenomenon not observed thus far in any non spatial Erdos-Renyi based planted partition models.

Control of queuing networks

One of the classical problems in queuing theory is to schedule customers/jobs in an optimal way. These problems are known as the scheduling problems. They arise in a wide variety of applications, in particular, whenever there are different customer classes present competing for the same resources. In a recent work “Ergodic control of multi-class M/M/N+M queues in the Halfin-Whitt regime”, Ari Arapostathis, Anup Biswas and Guodong Pang solved an ergodic control problem for multi-class many server queuing networks. The optimal solution of the queuing control problem can be approximated by that of the corresponding ergodic diffusion control problem in the limit. The proof technique introduces a new method of spatial truncation for the diffusion control problem.

Correlated shadowing in wireless stochastic geometry

Junse Lee, Xinchen Zhang and François Baccelli proposed new models for analyzing spatially correlated shadowing fields. These models allow one to analyze the interference field created by a wireless infrastructure through the walls and floors of a building with variable size rooms. These models provide a mathematical characterization of the interference distribution, which further leads to closed-from expressions for the coverage probability in cellular networks. Three network scenarios are studied: 2-D outdoor, 2-D indoor, and 3-D inbuilding.

Coverage in wireless networks

One of the most important geometric objects are the coverage regions of a transmitter or a set of transmitters. This question is jointly studied by Jeffrey Andrews, François Baccelli, Gustavo de Veciana, Robert Heath and Sanjay Shakkottai. Most of the initial steps are based on Poisson point processes. Lately, this continued with Yingzhe Li (Simons PhD student, ECE, UT Austin) to the case of determinantal point processes. Another line of work on studying cell-association problems in multi-technology cellular networks was carried out in this paper by Abishek Sankararaman, Jeong-woo Cho and François Baccelli.

 

Data driven discovery of sparse dynamics

Rachel Ward and collaborator Giang Tran (former Bing Instructor in the UT Mathematics department) are investigating the identification of a dynamical system (say, within the class of polynomial systems of ordinary differential equations) given snapshots of the system in time. Such problems prove challenging when there is a high level of noise on the data. In the paper Exact recovery of Chaotic systems from highly corrupted data, they show that if the underlying trajectory exhibits ergodicy or chaos, and if the underlying dynamics have a sparse representation with respect to the polynomial basis, then a LASSO / l1 type algorithm will exactly recover the underlying dynamics even when most of the data is highly corrupted. This establishes a new link between the areas of dynamical systems and machine learning / sparse recovery, and many interesting questions remain.

Detecting planted communities in random graphs

The stochastic block model (aka. planted partition model) is a popular model for representing networks with communities. Elchanan Mossel, Joe Neeman, and Allan Sly have been investigating algorithms and fundamental limits for detecting and recovering these communities. They established sharp transitions for the problem of extracting non-trivial information and the problem of exactly recovering communities. They also gave a new algorithm that obtains provably optimal accuracy for the problem of detecting communities in “Consistency thresholds for the planted bisection model” and “Belief propagation, robust reconstruction, and optimal recovery of block models“.

Entropy of point processes

François Baccelli and Jae Oh Woo initiated a study on the entropy and mutual information of point processes (On the Entropy and Mutual Information of Point Processes). The main new mathematical objects are the relative entropy rate and the mutual information rate of two stationary point processes. They also derived expression of the mutual information rate in the case of a homogeneous point process and its displacement. This machinery is used to revisit the Gaussian noise channel in the Shannon-Poltyrev regime recently introduced in Capacity and error exponents of stationary point processes under random additive displacements.

Extremes of spatial shot noise processes

Anup Biswas and François Baccelli studied the scaling limit of a class of shot-noise fields defined on an independently marked stationary Poisson point process and with a power law response function. Under appropriate conditions, they showed that the shot-noise field can be scaled suitably to have a non degenerate alpha-stable limit, as the intensity of the underlying point process goes to infinity. More precisely, finite dimensional distributions  converge and the finite dimensional distributions of the limiting random field have i.i.d. stable random components. This limit is hence called the alpha- stable white noise field. Analogous results are also obtained for the extremal shot-noise field which converges to a Fréchet white noise field.

Information theory and high dimensional stochastic geometry

The most basic capacity and error exponent questions of information theory can be expressed in terms of random geometric objects living in Euclidean spaces with dimensions tending to infinity. This approach was introduced by Venkat Anantharam and François Baccelli to evaluate random coding error exponents in channels with additive stationary and ergodic noise. More generally, the analysis of stochastic geometry in the Shannon regime leads to new high dimension stochastic geometry questions that are currently investigated. Eliza O’Reilly and and Francois Baccelli have also studied determinantal point processes in high dimensions. This work describes the strength and reach of repulsion of a typical point of certain parametric families of determinantal point process in the Shannon regime.

Mathematical problems in neuroscience

Recent advances in neuroscience provide theoretical neuroscientists with a vast wealth of new data and open questions related to information theory, high-dimensional geometry of representation and computation, and dynamics in the brain. The groups of Ila Fiete, Ngoc Mai Tran and Thibaud Taillefumier study these questions from analytical and numerical perspectives. Fiete and Tran have recently studied the learning capacity of neural networks (see “A binary Hopfield network with 1/\log(n) information rate and applications to grid cell decoding“, “ Robust exponential memory in Hopfield networks“, and “ Associative content-addressable networks with exponentially many robust stable states“).

Metastability of queuing networks with mobile servers

In a paper entitled Metastability of Queuing Networks with Mobile Servers, A. Rybko, S. Shlosman, A. Vladimorov and F. Baccelli study symmetric queuing networks with moving servers and FIFO service discipline. The mean-field limit dynamics demonstrates unexpected behavior which is attributed to the meta-stability phenomenon. Large enough finite symmetric networks on regular graphs are proved to be transient for arbitrarily small inflow rates. However, the limiting non-linear Markov process possesses at least two stationary solutions. The mean-field analysis is based on the Non Linear Markov Process developed for this type of queuing networks in Queuing Networks with Varying Topology – A Mean-Field Approach.

Moving user time series in SNR stochastic geometry

With Pranav Madadi, F. Baccelli, and G. de Veciana analyzed the temporal variations in the Shannon rate experienced by a user moving along a straight line in a cellular network represented by a Poisson-Voronoi tessellation. We consider a network that is shared by static users distributed as a Poisson point process and analyzed the time series of the final shared rate and the number of users sharing the network. The paper On Shared Rate Time Series for Mobile Users in Poisson Networks was focused on the noise limited case.

On the steady state of continuous time stochastic opinion dynamics

François Baccelli, Sriram Vishwanath and Jae Oh Woo proposed a computational framework for continuous time opinion dynamics with additive noise. They derived a non-local partial differential equation for the distribution of opinions differences. They used Mellin transforms to solve the stationary solution of this equation in closed form. This approach can be applied both to linear dynamics on an interaction graph and to bounded confidence dynamics in the Euclidean space. To the best of our knowledge, the closed form expression on the stationary distribution of the bounded confidence model is the first quantitative result on the equilibria of this class of models.

Optimization of DNA sequencing

High throughput DNA sequencing technology has greatly increased the speed and reduced the cost of genome sequencing. The process is divided into to two steps: generating a library of short reads and reassembling those reads into the original genome. Eliza O’Reilly, François Baccelli, Gustavo de Veciana, and Haris Vikalo have worked on modeling this process using stochastic geometry and queueing theory in order to optimize the output of correct reads and the probability of successful reassembly (see End-to-End Optimization of High Throughput DNA Sequencing).

Order parameters of dense phases

Dense phases of emergent systems, such as constrained complex networks,
exhibit distinct characteristics, the most studied being broken symmetry.
However for practical purposes “rigidity”, the resistance to change, is
also of wide interest. There are difficulties analyzing rigidity since
when perturbed a system can easily move out of its phase. A new approach to
overcome this contradiction has been initiated by David Aristoff and
Charles Radin: see the discussion in Quanta Magazine. Another characteristic
of dense phases are their nonspherical `Wulff’ shapes, polyhedral for ordinary crystals.
This is examined in this expository paper by Charles Radin, and in a related direction in this paper by Charles Radin, Kui Ren, and Lorenzo Sadun.

In a different direction, the process of nucleation is a dynamical signature of the creation of a dense phase, which can appear even in systems far removed from ordinary atomic materials, as shown in this paper by Frank Rietz, Charles Radin, Harry Swinney and Matthias Schroeter. All the above characteristics make essential use of finite systems, a nonstandard approach to understanding emergent phases.

Phases and phase transitions in complex networks

In the phenomenon of emergence, a system of many interacting objects
exhibits the collective behavior of one or more “phases”, which are
only detectable or even meaningful for the system as a whole. This is
an organizing principle widely used in biology, physics and indeed all
the sciences: crystals, hurricanes, animal flocking etc. One wants to
understand the spontaneous appearance of phases in systems of large
size, in particular to determine a mechanism of some generality. A
convenient framework for such an analysis is large networks with
constraints. Such an analysis has been undertaken by the group of
Richard Kenyon, Charles Radin, Kui Ren and Lorenzo Sadun, on entropy singularities, the edge/triangle system I, the edge/triangle system II, multipodal structure,
order-disorder transitions and oversaturated networks. There is also a related asymptotic analysis of large permutations undertaken by the group of Richard Kenyon, Daniel Kral, Charles Radin and Peter Winkler: permutations with fixed pattern densities, and a review of phases in general combinatorial systems.

Point maps on point processes

A compatible point-shift f maps, in a translation invariant way, each point of a stationary point process N to some point of N. It is fully determined by its associated point-map, g, which gives the image of the origin by f. The initial question studied by Mir-Omid Haji-Mirsadeghi and François Baccelli is whether there exist probability measures which are left invariant by the translation of -g. The point map probabilities of N are defined from the action of the semigroup of point-map translations on the space of Palm probabilities, and more precisely from the compactification of the orbits of this semigroup action. If the point-map probability is uniquely defined, and if it satisfies certain continuity properties, it then provides a solution to the initial question. Point-map probabilities are shown to be a strict generalization of Palm probabilities: when the considered point-shift f is bijective, the point-map probability of N boils down to the Palm probability of N. When it is not bijective, there exist cases where the point-map probability of N is absolutely continuous with respect to its Palm probability, but there also exist cases where it is singular with respect to the latter.

Each such point-shift defines a random graph on the points of the point process. The connected components of this graph can be split into a collection of foils, which are the analogue of the stable manifold of the point-shift dynamics.
The same authors give a general classification of point-shifts in terms of the cardinality of the foils of these connected components. There are three types: F/F, I/F and I/I as shown in the paper Point-Shift Foliation of a Point Process.

Point processes on topological groups

Using the framework of Günter Last, James Murphy has extended the cardinality classification to the case of point processes on unimodular groups. J. Murphy has studied point-shifts of point processes on topological groups at length.

Poisson Hail

Consider a queue where the server is the Euclidean space, the customers are random closed sets (RACS) of the Euclidean space. These RACS arrive according to a Poisson rain and each of them has a random service time (in the case of hail falling on the Euclidean plane, this is the height of the hailstone, whereas the RACS is its footprint). The Euclidean space serves customers at speed 1. The service discipline is a hard exclusion rule: no two intersecting RACS can be served simultaneously and service is in the First In First Out order (only the hailstones in contact with the ground melt at speed 1, whereas the other ones are queued; a tagged RACS waits until all RACS arrived before it and intersecting it have fully melted before starting its own melting). We prove that this queue is stable for a sufficiently small arrival intensity, provided the typical diameter of the RACS and the typical service time have finite exponential moments.

In Shape Theorems For Poisson Hail on a Bivariate Ground, H. Chang, S. Foss and F. Baccelli have extended this Poisson Hail model to the situation where the service speed is either zero or infinity at each point of the Euclidean space. Tools pertaining to sub-additive ergodic theory are used to establish shape theorems for the growth of the ice-heap under light tail assumptions on the hailstone characteristics. The asymptotic shape depends on the statistics of the hailstones, the intensity of the underlying Poisson point process and on the geometrical properties of the zero speed set.

Scaling laws for ergodic spectral efficiency in MIMO Poisson networks

Ergodic spectral efficiency quantifies the achievable Shannon transmission rate per unit area, and captures the effects of rate adaptation techniques. Junse Lee, Namyoon Lee and François Baccelli studied the benefits of multiple antenna communication in ad-hoc networks using this metric. In this work, the primary finding is that, with knowledge of channel state information between a receiver and its associated transmitter, the ergodic spectral efficiency can be made to scale linearly with both 1) the minimum of the number of transmit and receive antennas and 2) the density of nodes. This scaling law is achieved when the multiple transmit antennas send multiple data streams and the multiple receive antennas are leveraged to cancel interference. Spatial multiplexing transmission methods are shown to be essential for obtaining better and eventually optimal scaling laws in such random wireless networks.

Spatial birth and death processes

Spatial point processes involving birth and death dynamics are ubiquitous in networks. Such dynamics are particularly important in peer-to-peer networks and in wireless networks. In the paper “Mutual Service Processes in R^d, Existence and Ergodicity”, Fabien Mathieu (Bell Laboratories), Ilkka Norros (VTT) and François Baccelli proposed a way to analyze the long term behavior of such dynamics on Euclidean spaces using coupling techniques. This line of though is continued by Mayank Manjrekar on other classes of processes like hard core spatial birth and death processes.

Spatial Birth-Death Wireless Networks

In this paper, Abishek Sankararaman and François Baccelli introduce a new form of spatial dynamics motivated by ad-hoc wireless networks. They study a birth death process where particles arrive in space as a Poisson process in space and time, and depart the system on completion of file transfer. The instantaneous rate of file transfer of any link is given by the Shannon formula of treating interference as noise. As the instantaneous interference seen by a link is dependent on the configuration of links present, this dynamics is an example of one where dynamics shapes geometry and in turn the geometry shapes the dynamics. In this paper, the authors establish a sharp phase-transition for stability of such dynamics. Moreover, whenever such dynamics is stable, they prove that the steady state is a clustered point process. Through simulations, they also argue that such dynamics cannot be simplified to any form of non-spatial queuing type dynamics.

Stochastic opinion dynamics in social networks

Avhishek Chatterjee, François Baccelli and Sriram Vishwanath proposed a stochastic extension of the bounded confidence model where opinions take their values in the Euclidean space and where friendship and interactions are dynamically defined through time varying and random neighborhoods. Two basic sub-models are defined: the influencing model where each agent is an attractor to the opinions of its neighbors and the listening model where each agent gathers information from others to update its own opinions. The general model contains a rich set of variants for which they proposed a classification. They analyzed the stability of its dynamics. The analysis highlights the need of certain leaders with heavy tailed neighborhoods for stability to hold. See Pairwise Stochastic Bounded Confidence Opinion Dynamics: Heavy Tails and Stability

Synchrony in stochastic spiking neural networks

Neural systems propagate information via neuronal networks that transform sensory input into distributed spiking patterns, and dynamically process these patterns to generate behaviorally relevant responses. The presence of noise at every stage of neural processing imposes serious limitation on the coding strategies of these networks. In particular, coding information via spike timings, which presumably achieves the highest information transmission rate, requires neural assemblies to exhibit high level of synchrony. Thibaud Taillefumier and collaborators are interested in understanding how synchronous activity emerges in modeled populations of spiking neurons, focusing on the interplay between driving inputs and network structure. Their approach relies on methods from Markov chain, point processes, and diffusion processes theories, in combination with exact event-driven simulation techniques. The ultimate goal is two-fold: 1) to identify the input/structure relations that optimize information transmission capabilities and 2) to characterize the “physical signature’’ of such putative optimal tunings in recorded spiking activity.

Vertex Shifts on Random Graphs

 

Ali Khezeli, Mir-Omid Haji-Mirsadeghi and François Baccelli studied dynamics on the vertices of a random graph in the paper Dynamics on Unimodular Graphs. The first result of the paper is a classification of vertex-shifts on unimodular random networks. Each such vertex-shift partitions the vertices into a collection of connected components and foils, as in the case of point-shifts on point processes.
The classification is based on the cardinality of the connected components and foils. Up to an event of zero probability, there are three types of foliations in a connected component: F/F (with finitely many finite foils), I/F (infinitely many finite foils), and I/I (infinitely many infinite foils).
An infinite connected component of the graph of a vertex-shift on a random network forms an infinite directed tree with one selected end which is referred to as an Eternal Family Tree. An Eternal Family Tree contains a subtree which is a stochastic generalization of a branching process. In a unimodular Eternal Family Tree, the subtree in question is a generalization of a critical branching process. In a $\sigma$-invariant Eternal Family Tree, the subtree is a generalizisation of a non-necessarily critical branching process. The latter trees allow one to analyze dynamics on networks which are not necessarily unimodular.

Zeros of Random Tropical Polynomials

Ngoc Mai Tran and François Baccelli derived a tropical version of the result of Kac on the zeros of polynomials with random coefficients (Zeros of Random Tropical Polynomials, Random Polygons and Stick-Breaking). 

The common roots of tropical of a class of random polynomials in two variables is analyzed in a recent work by the same authors in Iterated Gilbert Mosaics and Poisson Tropical Plane Curves using a stochastic geometry approach based on iterated random tessellations.

Publications

2017_1000869 2017_1000829

On solutions of mean field games with ergodic cost

Ari Arapostathis, Anup Biswas, and Johnson Carroll Journal de Mathematiques Pures et Appliquees vol. 107, no. 5, pp. 205-251, 2017
2017-09-04_1001023

Surface effects in dense random graphs with sharp edge constraint

Charles Radin, Kui Ren and Lorenzo Sadun Arxiv 2017
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2017-07-30_1001012 2017-06-15_1000985

Nucleation of athermal hard spheres

F. Rietz, C. Radin, H. Swinney and M. Schroeter, ArXiv 2017
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2017-06-15_1000983

The phases of large networks with edge and triangle constraints

R. Kenyon, C. Radin, K. Ren and L. Sadun ArXiv 2017
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2017-06-15_1000981

The open mathematics of crystallization

Charles Radin Notices Amer. Math. Soc. 64(2017) 551-556
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2017-06-01_1000994

Spatial Birth Death Wireless Networks

Abishek Sankararaman and François Baccelli IEEE Transactions on Information Theory 63(6): 3964-3982 (2017)
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2017-05-09_1000975 2017-05-09_1000911

Pairwise Stochastic Bounded Confidence Opinion Dynamics: Heavy Tails and Stability

François Baccelli, Avhishek Chatterjee, and Sriram Vishwanath To appear in IEEE Transactions Automatic Control
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2017-04-14_1000789

Metastability of Queuing Networks with Mobile Servers

F. Baccelli, A. Rybko, S. Shlosman, A. Vladimirov https://arxiv.org/abs/1704.02521
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2017-04-01_1000817

SINR and Throughput of Dense Cellular Networks with Stretched Exponential Path Loss

Ahmad AlAmmouri, Jeffrey G. Andrews, and François Baccelli Submitted to IEEE Transactions on Wireless Communications
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2016_1000824

The Dirichlet problem for stable-like operators and probabilistic representations

Ari Arapostathis, Anup Biswas, and Luis Caffarelli Communications in Partial Differential Equations vol. 41, no. 9, pp. 1472-1511, 2016
2016_1000818

Risk-sensitive control and an abstract Collatz-Wielandt formula

Ari Arapostathis, Vivek S. Borkar, and K. Suresh Kumar Journal of Theoretical Probability vol. 29, no. 4, pp. 1458-1484, 2016
2016_1000816

Ergodic Diffusion Control of Multiclass Multi-Pool Networks in the Halfin–Whitt Regime

Ari Arapostathis and Guodong Pang Annals of Applied Probability vol. 26, no. 5, pp. 3110– 3153, 2016
2016_1000621

Performance-Oriented Association in Large Cellular Networks with Technology Diversity

Abishek Sankararaman, Jeong-woo Cho, François Baccelli 28th International Teletraffic Congress (ITC 28), 2016
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2016_1000586

End-to-End Optimization of High Throughput DNA Sequencing

Eliza O'Reilly, Francois Baccelli, Gustavo de Veciana, Haris Vikalo Journal of Computational Biology 2016
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2016-10-16_1000749

Iterated Gilbert Mosaics and Poisson Tropical Plane Curves

Francois Baccelli and Ngoc Tran https://arxiv.org/abs/1610.08533
2016-09-25_1000612

A 3-D Spatial Model for In-building Wireless Networks with Correlated Shadowing

Junse Lee, Xinchen Zhang, Francois Baccelli Accepted to IEEE Transactions on Wireless Communications
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2016-09-18_1000754

On Shared Rate Time Series for Mobile Users in Poisson Networks

Pranav Madadi, François Baccelli, Gustavo de Veciana https://arxiv.org/abs/1609.08845
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2016-08-22_1000655

Scaling Laws for Ergodic Spectral Efficiency in MIMO Poisson Networks

Junse Lee, Namyoon Lee, Francois Baccelli Submitted to IEEE Transactions on Information Theory
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2016-08-21_1000762

Dynamics on Unimodular Random Graphs

François Baccelli, Mir-Omid Haji-Mirsadeghi, Ali Khezeli https://arxiv.org/abs/1608.05940
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2016-08-11_1000660

On the Entropy and Mutual Information of Point Processes

François Baccelli, Jae Oh Woo IEEE International Symposium on Information Theory 2016/695-699
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2016-06-15_1000936

Exact Recovery of Chaotic Systems from Highly Corrupted Data

Giang Tran, Rachel Ward To appear in SIAM Multiscale Modeling and Simulation
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2016-05-20_1000948

Shape Theorems For Poisson Hail on a Bivariate Ground

Francois Baccelli, Hector A. Chang-Lara, and Sergey Foss ArXiv 2016
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2016-05-01_1000915 2016-04-27_1000640

A symmetry breaking transition in the edge/triangle network model

Charles Radin, Kui Ren and Lorenzo Sadun Arxiv 2016
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2016-01-19_1000533 2016-01-14_1000623

Point-Shift Foliation of a Point Process

Francois Baccelli and Mir-Omid Haji-Mirsadeghi ArXiv 2016
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2015-10-21_1000150

Ergodic Control of Multi-class M/M/N+M Queues in the Halfin-Whitt Regime

Ari Arapostathis, Anup Biswas and Guodong Pang Annals of Applied Probability vol. 25, no. 6, pp. 3511-3570, 2015
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2015-09-18_1000522

Bipodal structure in oversaturated random graphs

R. Kenyon, C. Radin, K. Ren and L. Sadun ArXiv 2015
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2015-09-13_1000135

Zeros of Random Tropical Polynomials, Random Polygons and Stick-Breaking

François Baccelli and Ngoc Mai Tran To appear in the Transctions of the AMS 2015
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2015-09-01_1000506

Permutations with fixed pattern densities

R. Kenyon, D. Kral, C. Radin, P. Winkler ArXiv 2015
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2015-06-18_1000441 2015-06-16_1000329

Consistency thresholds for the planted bisection model

Elchanan Mossel, Joe Neeman and Allan Sly To appear in Symposium on the Theory of Computer Science 2015
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2015-05-28_1000151

On Scaling Limits of Power Law Shot-noise Fields

François Baccelli and Anup Biswas To appear in Stochastic Models 2015
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2015-04-30_1000324

Pairwise Stochastic Bounded Confidence Opinion Dynamics: Heavy Tails and Stability

François Baccelli, Avhishek Chatterjee and Sriram Vishwanath Proceedings IEEE Infocom 2015
2015-04-28_1000153

Controlled Equilibrium Selection in Stochastically Perturbed Dynamics

Ari Arapostathis, Anup Biswas and Vivek Borkar Arxiv 2015
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2015-04-21_1000233

Capacity and Error Exponents of Stationary Point Processes under Random Additive Displacements

Venkat Anantharam and François Baccelli Advances Applied Prob. Volume 47, Number 1, 2015
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2015-03-01_1000218

Singularities in the Entropy of Asymptotically Large Simple Graphs

Charles Radin and Lorenzo Sadun J. Stat. Phys. 158 (2015) 853-865
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2015-01-22_1000165

Resource Allocation: Realizing Mean-Variability-Fairness Tradeoffs

Vinay Joseph, Gustavo de Veciana and Ari Arapostathis IEEE Transactions on Automatic Control 60(1):19-33 January 2015
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2014-12-05_1000282

Statistical Modeling and Probabilistic Analysis of Cellular Networks with Determinantal Point Processes

Yingzhe Li, François Baccelli, Harpreet S. Dhillon and Jeffrey G. Andrews Arxiv 2014
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2014-11-17_1000382

Robust exponential memory in Hopfield networks

Christopher Hillar and Ngoc Mai Tran arXiV 2014
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2014-10-28_1000283 2014-08-06_1000286

The Boolean Model in the Shannon Regime: Three Thresholds and Related Asymptotics

Venkat Anantharam and François Baccelli Arxiv 2014 To appear in Advances in Applied Probability
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2014-07-22_1000381

A binary Hopfield network with 1/\log(n) information rate and applications to grid cell decoding

Ila Fiete, David J. Schwab and Ngoc M Tran 2nd Workshop on Biological Distributed Algorithms 2014
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2014-07-19_1000288

Compactification of the Action of a Point-Map on the Palm Probability of a Point Process

François Baccelli and Mir-Omid Haji-Mirsadeghi Arxiv 2014 To appear in the Annals of Probability
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2014-07-16_1000167

Spatial Reuse and Fairness in Ad Hoc Networks with Channel-Aware CSMA Protocols

Yuchul Kim, François Baccelli and Gustavo de Veciana IEEE Transactions on Information Theory 60(7):4139-57, July 2014
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2014-06-18_1000333

Belief propagation, robust reconstruction, and optimal recovery of block models

Elchanan Mossel, Joe Neeman and Allan Sly Conference on Learning Theory 2014
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2014-06-15_1000331

Testing surface area with arbitrary accuracy

Joe Neeman Symposium on the Theory of Computer Science 2014
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2014-04-19_1000152

On a Class of Stochastic Differential Equations With Jumps and its Properties

Ari Arapostathis, Anup Biswas and Luis Caffarelli Arxiv 2014
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2014-04-13_1000212

The Asymptotics of Large Constrained Graphs

Charles Radin, Kui Ren and Lorenzo Sadun J. Phys. A: Math. Theor. 47 (2014) 175001
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2014-04-10_1000209

Multipodal Structure and Phase Transitions in Large Constrained Graphs

Richard Kenyon, Charles Radin, Kui Ren and Lorenzo Sadun ArXiv 2014
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2014-04-08_1000146

Mutual Service Processes in R^d, Existence and Ergodicity

François Baccelli, Fabien Mathieu and Ilkka Norros ArXiv 2014
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2014-02-28_1000830

Convergence of the relative value iteration for the ergodic control problem of nondegenerate diffusions under near- monotone costs

Ari Arapostathis, Vivek S. Borkar, and K. Suresh Kumar vol. 52, no. 1, pp. 1–31, 2014
2013-11-15_1000291

Queuing Networks with Varying Topology – A Mean-Field Approach

François Baccelli, Alexandre Rybko and Senya Shlosman Arxiv 2013
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2000-01-01_1000992

Community Detection on Euclidean Random Graphs

Abishek Sankararaman and François Baccelli ArXiv 2017
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