Ali H. Sayed

Bio: Ali H. Sayed is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Adaptive filter & Optimization problem. The author has an hindex of 81, co-authored 728 publications receiving 36030 citations. Previous affiliations of Ali H. Sayed include Harbin Engineering University & University of California, Los Angeles.

Excess-Risk of Distributed Stochastic Learners

Abstract: This work studies the learning ability of consensus and diffusion distributed learners from continuous streams of data arising from different but related statistical distributions. Four distinctive features for diffusion learners are revealed in relation to other decentralized schemes even under left-stochastic combination policies. First, closed-form expressions for the evolution of their excess-risk are derived for strongly-convex risk functions under a diminishing step-size rule. Second, using these results, it is shown that the diffusion strategy improves the asymptotic convergence rate of the excess-risk relative to non-cooperative schemes. Third, it is shown that when the in-network cooperation rules are designed optimally, the performance of the diffusion implementation can outperform that of naive centralized processing. Finally, the arguments further show that diffusion outperforms consensus strategies asymptotically, and that the asymptotic excess-risk expression is invariant to the particular network topology. The framework adopted in this work studies convergence in the stronger mean-square-error sense, rather than in distribution, and develops tools that enable a close examination of the differences between distributed strategies in terms of asymptotic behavior, as well as in terms of convergence rates.

Order-adaptive frequency trackers for direct-to-Earth Mars communications

Abstract: During the entry, descent and landing phase (EDL) of the missions to Mars, the spacecraft's high dynamics imprints severe Doppler swings on the signals transmitted via the direct-to-Earth (DTE) channel. In order to recover the data that record the mission status from the received signal, a reliable estimate of the Doppler profile is required. We extend previous work by developing order-adaptive schemes that enforce frequency continuity and improve tracking performance and, as a result, the overall frequency mean-square error as well.

Finite Bit Quantization for Decentralized Learning Under Subspace Constraints

Abstract: In this paper, we consider decentralized optimization problems where agents have individual cost functions to mini-mize subject to subspace constraints that require the min-imizers across the network to lie in low-dimensional sub-spaces. This constrained formulation includes consensus optimization as special case, and allows for more general task relatedness models such as multitask smoothness and coupled optimization. In order to cope with communication constraints, we propose and study a quantized differential based approach where the communicated estimates among agents are quantized. The analysis shows that, under some general conditions on the quantization noise, and for sufficiently small step-sizes µ, the strategy is stable in the mean-square error sense. The analysis also reveals the influence of the gradient and quantization noises on the performance.

Learning by networked agents under partial information

Abstract: In many scenarios of interest, agents may only have access to partial information about an unknown model or target vector. Each agent may be sensing only a subset of the entries of a global target vector, and the number of these entries can be different across the agents. If each of the agents were to solve an inference task independently of the other agents, then they would not benefit from neighboring agents that may be sensing similar entries. This work develops cooperative distributed techniques that enable agents to cooperate even when their interactions are limited to exchanging estimates of select few entries. In the proposed strategies, agents are only required to share estimates of their common entries, which results in a significant reduction in communication overhead. Simulations show that the proposed approach improves both the performance of individual agents and the entire network through cooperation.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Tensor Decompositions and Applications

Abstract: This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Table Of Integrals Series And Products

Abstract: Thank you very much for downloading table of integrals series and products. Maybe you have knowledge that, people have look hundreds times for their chosen books like this table of integrals series and products, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. table of integrals series and products is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the table of integrals series and products is universally compatible with any devices to read.