16 research outputs found
Group-Lasso on Splines for Spectrum Cartography
The unceasing demand for continuous situational awareness calls for
innovative and large-scale signal processing algorithms, complemented by
collaborative and adaptive sensing platforms to accomplish the objectives of
layered sensing and control. Towards this goal, the present paper develops a
spline-based approach to field estimation, which relies on a basis expansion
model of the field of interest. The model entails known bases, weighted by
generic functions estimated from the field's noisy samples. A novel field
estimator is developed based on a regularized variational least-squares (LS)
criterion that yields finitely-parameterized (function) estimates spanned by
thin-plate splines. Robustness considerations motivate well the adoption of an
overcomplete set of (possibly overlapping) basis functions, while a sparsifying
regularizer augmenting the LS cost endows the estimator with the ability to
select a few of these bases that ``better'' explain the data. This parsimonious
field representation becomes possible, because the sparsity-aware spline-based
method of this paper induces a group-Lasso estimator for the coefficients of
the thin-plate spline expansions per basis. A distributed algorithm is also
developed to obtain the group-Lasso estimator using a network of wireless
sensors, or, using multiple processors to balance the load of a single
computational unit. The novel spline-based approach is motivated by a spectrum
cartography application, in which a set of sensing cognitive radios collaborate
to estimate the distribution of RF power in space and frequency. Simulated
tests corroborate that the estimated power spectrum density atlas yields the
desired RF state awareness, since the maps reveal spatial locations where idle
frequency bands can be reused for transmission, even when fading and shadowing
effects are pronounced.Comment: Submitted to IEEE Transactions on Signal Processin
Quadratic approximate dynamic programming for scheduling water resources: a case study
We address the problem of scheduling water resources in a power system via
approximate dynamic programming.To this goal, we model a finite horizon
economic dispatch problemwith convex stage cost and affine dynamics, and
consider aquadratic approximation of the value functions. Evaluating
theachieved policy entails solving a quadratic program at each timestep, while
value function fitting can be cast as a semidefiniteprogram. We test our
proposed algorithm on a simplified versionof the Uruguayan power system,
achieving a four percent costreduction with respect to the myopic polic
Inference of Gene Regulatory Networks with Sparse Structural Equation Models Exploiting Genetic Perturbations
<div><p>Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs) are employed to integrate both gene expression data and <i>cis</i>-expression quantitative trait loci (<i>cis</i>-eQTL), for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML) is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL) based scheme, and the QTL-directed dependency graph (QDG) method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.</p></div
Performance of the SML and AL algorithms for directed <i>acyclic</i> networks of genes [(a) power of detection, and (b) false discover rate].
<p>Expected number of nodes per node is . PD and FDR were obtained from 10 replicates of the network with different sample sizes ( to 1,000).</p
Performance of SML, AL and QDG algorithms for directed <i>acyclic</i> networks of [(a) and (b)] or 30 [(c) and (d)] genes.
<p>Expected number of nodes per node is . PD and FDR were obtained from 100 replicates of the network with different sample sizes ( to 1,000).</p
Performance of SML, AL and QDG algorithms for directed <i>cyclic</i> networks of [(a) and (b)] or 30 [(c) and (d)] genes.
<p>Expected number of nodes per node is . PD and FDR were obtained from 100 replicates of the network with different sample sizes ( to 1,000).</p