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Steady-State Performance of Adaptive Diffusion Least-Mean Squares

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Adaptive Networks

Ali H. Sayed

This paper surveys recent advances related to adaptation, learning, and optimization over networks. Various distributed strategies are discussed that enable a collection of networked agents to interact locally in response to streaming data and to continual ...

IEEE2014

Diffusion LMS for Clustered Multitask Networks

Ali H. Sayed

Recent research works on distributed adaptive networks have inten- sively studied the case where the nodes estimate a common parame- ter vector collaboratively. However, there are many applications that are multitask-oriented in the sense that there are mu ...

2014

The Squares of Montchoisy. Notes for a History of the Open Form in Geneva

Elena Cogato Lanza

This paper deals with the ambivalent character of the neighborhood «Squares Montchoisy» built in Geneva between 1929 and 1957. According to visual representations (photographs and drawings) disseminated by the architect Maurice Braillard, the project embod ...

2014

Bayesian Estimation for Continuous-Time Sparse Stochastic Processes

Michaël Unser, Arash Amini, Emrah Bostan, Ulugbek Kamilov

We consider continuous-time sparse stochastic processes from which we have only a finite number of noisy/noiseless samples. Our goal is to estimate the noiseless samples (denoising) and the signal in-between (interpolation problem). By relying on tools fro ...

Ieee-Inst Electrical Electronics Engineers Inc2013

MMSE Estimation of Sparse Levy Processes

Michaël Unser, Arash Amini, Ulugbek Kamilov, Pedram Pad

We investigate a stochastic signal-processing framework for signals with sparse derivatives, where the samples of a Levy process are corrupted by noise. The proposed signal model covers the well-known Brownian motion and piecewise-constant Poisson process; ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Bayesian Denoising: From MAP to MMSE Using Consistent Cycle Spinning

Michaël Unser, Emrah Bostan, Ulugbek Kamilov, Abbas Kazerouni

We introduce a new approach for the implementation of minimum mean-square error (MMSE) denoising for signals with decoupled derivatives. Our method casts the problem as a penalized least-squares regression in the redundant wavelet domain. It exploits the l ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Intricacies of Describing Weak Interactions involving Halogen Atoms within Density Functional Theory

Ursula Röthlisberger, Ivano Tavernelli

In this work we assess the performance of different dispersion-corrected DFT approaches (M06, M06-2X, DFT-D3 and DCACP) in reproducing high-level wave function based benchmark calculations on the weakly bound halogen dimers (X2)2 and X2-Ar (for X=F,Cl,Br,I ...

Amer Chemical Soc2013

When crowding of crowding leads to uncrowding

Michael Herzog, Bilge Sayim, Mauro Manassi

In object recognition, features are thought to be processed in a hierarchical fashion from low-level analysis (edges and lines) to complex figural processing (shapes and objects). Here, we show that figural processing determines low-level processing. Verni ...

Association for Research in Vision and Ophthalmology2013

Diffusion Strategies Outperform Consensus Strategies for Distributed Estimation Over Adaptive Networks

Ali H. Sayed

Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes interact with each other on a local level and diffuse information across the network to solve estimation and inference tasks in a distributed manner. In th ...

IEEE2012

Performance Limits for Distributed Estimation Over LMS Adaptive Networks

Ali H. Sayed

In this work, we analyze the mean-square performance of different strategies for distributed estimation over least-mean-squares (LMS) adaptive networks. The results highlight some useful properties for distributed adaptation in comparison to fusion-based c ...

IEEE2012