@article{Valera2016ab,

title = {Infinite Continuous Feature Model for Psychiatric Comorbidity Analysis.},

author = {Isabel Valera and Francisco J R Ruiz and Pablo M Olmos and Carlos Blanco and Fernando Perez-Cruz},

url = {http://www.ncbi.nlm.nih.gov/pubmed/26654208},

doi = {10.1162/NECO_a_00805},

issn = {1530-888X},

year  = {2016},

date = {2016-02-01},

journal = {Neural computation},

volume = {28},

number = {2},

pages = {354--381},

abstract = {We aim at finding the comorbidity patterns of substance abuse, mood and personality disorders using the diagnoses from the National Epidemiologic Survey on Alcohol and Related Conditions database. To this end, we propose a novel Bayesian nonparametric latent feature model for categorical observations, based on the Indian buffet process, in which the latent variables can take values between 0 and 1. The proposed model has several interesting features for modeling psychiatric disorders. First, the latent features might be off, which allows distinguishing between the subjects who suffer a condition and those who do not. Second, the active latent features take positive values, which allows modeling the extent to which the patient has that condition. We also develop a new Markov chain Monte Carlo inference algorithm for our model that makes use of a nested expectation propagation procedure.},

keywords = {Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Durisi2016b,

title = {Short-Packet Communications Over Multiple-Antenna Rayleigh-Fading Channels},

author = {Giuseppe Durisi and Tobias Koch and Johan Ostman and Yury Polyanskiy and Wei Yang},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7362178},

doi = {10.1109/TCOMM.2015.2511087},

issn = {0090-6778},

year  = {2016},

date = {2016-02-01},

journal = {IEEE Transactions on Communications},

volume = {64},

number = {2},

pages = {618--629},

publisher = {IEEE},

abstract = {Motivated by the current interest in ultra-reliable, low-latency, machine-type communication systems, we investigate the tradeoff between reliability, throughput, and latency in the transmission of information over multiple-antenna Rayleigh block-fading channels. Specifically, we obtain finite-blocklength, finite-SNR upper and lower bounds on the maximum coding rate achievable over such channels for a given constraint on the packet error probability. Numerical evidence suggests that our bounds delimit tightly the maximum coding rate already for short blocklengths (packets of about 100 symbols). Furthermore, our bounds reveal the existence of a tradeoff between the rate gain obtainable by spreading each codeword over all available time-frequency-spatial degrees of freedom, and the rate loss caused by the need of estimating the fading coefficients over these degrees of freedom. In particular, our bounds allow us to determine the optimal number of transmit antennas and the optimal number of time-frequency diversity branches that maximize the rate. Finally, we show that infinite-blocklength performance metrics such as the ergodic capacity and the outage capacity yield inaccurate throughput estimates},

keywords = {diversity branches, Encoding, ergodic capacity, Fading, fading channels, finite-blocklength information theory, finiteblocklength information theory, infinite-blocklength performance metrics, Journal, machine-type communication systems, maximum coding rate, Mission critical systems, mission-critical machine-type communications, multiple antennas, multiple-antenna Rayleigh block-fading channels, Multiplexing, optimal number, outage capacity, rate gain, Rayleigh channels, Receivers, Reliability, short-packet communications, spatial multiplexing, Throughput, Time-frequency analysis, time-frequency-spatial degrees of freedom, transmit antennas, transmit diversity, Transmitting antennas, Ultra-reliable low-latency communications},

pubstate = {published},

tppubtype = {article}

}

@article{Valera2016c,

title = {Infinite Factorial Unbounded-State Hidden Markov Model},

author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},

url = {http://www.ncbi.nlm.nih.gov/pubmed/26571511 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true\&arnumber=7322279},

doi = {10.1109/TPAMI.2015.2498931},

issn = {1939-3539},

year  = {2016},

date = {2016-01-01},

journal = {IEEE transactions on pattern analysis and machine intelligence},

volume = {To appear},

number = {99},

pages = {1},

abstract = {There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or the number of states of the FHMM cannot be known or limited a priori. In this paper, we propose an infinite factorial unbounded-state hidden Markov model (IFUHMM), in which the number of parallel hidden Markov models (HMMs) and states in each HMM are potentially unbounded. We rely on a Bayesian nonparametric (BNP) prior over integer-valued matrices, in which the columns represent the Markov chains, the rows the time indexes, and the integers the state for each chain and time instant. First, we extend the existent infinite factorial binary-state HMM to allow for any number of states. Then, we modify this model to allow for an unbounded number of states and derive an MCMC-based inference algorithm that properly deals with the trade-off between the unbounded number of states and chains. We illustrate the performance of our proposed models in the power disaggregation problem.},

keywords = {Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium},

pubstate = {published},

tppubtype = {article}

}

@article{Nazabal2016bb,

title = {Human Activity Recognition by Combining a Small Number of Classifiers},

author = {Alfredo Nazabal and Pablo Garcia-Moreno and Antonio Artes-Rodriguez and Zoubin Ghahramani},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7161292},

doi = {10.1109/JBHI.2015.2458274},

issn = {2168-2208},

year  = {2016},

date = {2016-01-01},

journal = {IEEE journal of biomedical and health informatics},

volume = {To appear},

publisher = {IEEE},

abstract = {We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.},

keywords = {Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Sensor systems},

pubstate = {published},

tppubtype = {article}

}

@article{Borchani2016,

title = {Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers},

author = {Hanen Borchani and Pedro Larra\~{n}aga and J Gama and Concha Bielza},

url = {http://cig.fi.upm.es/node/879},

year  = {2016},

date = {2016-01-01},

journal = {Intelligent Data Analysis},

volume = {20},

keywords = {CASI CAM CM, CIG UPM, Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Asheghan2016,

title = {Stability Analysis and Robust Control of Heart Beat Rate During Treadmill Exercise},

author = {Mohammad Mostafa Asheghan and Joaqu\'{i}n M\'{i}guez},

doi = {10.1016/j.automatica.2015.10.027},

issn = {00051098},

year  = {2016},

date = {2016-01-01},

journal = {Automatica},

volume = {63},

pages = {311--320},

abstract = {We investigate a nonlinear dynamical model of a human's heart beat rate (HBR) during a treadmill exercise. We begin with a rigorous analysis of the stability of the model that extends significantly the results available in the literature. In particular, we first identify a simple set of necessary and sufficient conditions for both input-state stability and Lyapunov stability of the system, and then prove that the same conditions also hold when the model parameters are subject to unknown but bounded perturbations. The second part of the paper is devoted to the design and analysis of a control structure for this model, where the treadmill speed plays the role of the control input and the output is the subject's HBR, which is intended to follow a prescribed pattern. We propose a simple control scheme, suitable for a practical implementation, and then analyze its performance. Specifically, we prove (i) that the same conditions that guarantee the stability of the system also ensure that the controller attains a desired level of performance (quantified in terms of the admissible deviation of the HBR from the prescribed profile) and (ii) that the controller is robust to bounded perturbations both in the system parameters and the control input. Numerical simulations are also presented in order to illustrate some of the theoretical results.},

keywords = {Cardiovascular system, Journal, Nonlinear systems, Robust control},

pubstate = {published},

tppubtype = {article}

}

@article{F.Pradier2016,

title = {Prior Design for Dependent Dirichlet Processes: An Application to Marathon Modeling},

author = {Melanie F. Pradier and Francisco J R Ruiz and Fernando Perez-Cruz},

editor = {Guy Brock},

url = {http://dx.plos.org/10.1371/journal.pone.0147402},

doi = {10.1371/journal.pone.0147402},

issn = {1932-6203},

year  = {2016},

date = {2016-01-01},

journal = {PLOS ONE},

volume = {11},

number = {1},

pages = {e0147402},

publisher = {Public Library of Science},

abstract = {This paper presents a novel application of Bayesian nonparametrics (BNP) for marathon data modeling. We make use of two well-known BNP priors, the single-p dependent Dirichlet process and the hierarchical Dirichlet process, in order to address two different problems. First, we study the impact of age, gender and environment on the runners' performance. We derive a fair grading method that allows direct comparison of runners regardless of their age and gender. Unlike current grading systems, our approach is based not only on top world records, but on the performances of all runners. The presented methodology for comparison of densities can be adopted in many other applications straightforwardly, providing an interesting perspective to build dependent Dirichlet processes. Second, we analyze the running patterns of the marathoners in time, obtaining information that can be valuable for training purposes. We also show that these running patterns can be used to predict finishing time given intermediate interval measurements. We apply our models to New York City, Boston and London marathons.},

keywords = {Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Guzman2015,

title = {Cooperative Optical Wireless Transmission for Improving Performance in Indoor Scenarios for Visible Light Communications},

author = {Borja Genoves Guzman and Alejandro Lancho Serrano and V\'{i}ctor Gil P Jimenez},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7389772},

doi = {10.1109/TCE.2015.7389772},

issn = {0098-3063},

year  = {2015},

date = {2015-11-01},

journal = {IEEE Transactions on Consumer Electronics},

volume = {61},

number = {4},

pages = {393--401},

publisher = {IEEE},

abstract = {In this paper, a novel cooperative transmission and reception scheme in Visible Light Communications (VLC) is proposed and evaluated. This new scheme provides improvements and reliability in large indoor scenarios, such as corridors, laboratories, shops or conference rooms, where the coverage needs to be obtained by using different access points when VLC is used. The main idea behind the proposal is a simple cooperative transmission scheme where the receiver terminal will obtain the signal from different access points at the same time. This proposal outperforms traditional VLC schemes, especially in Non-Line-of-Sight reception where around 3 dB of gain, with respect to traditional schemes, can be obtained for unoptimized parameters, and larger than 3 dB could easily be achieved. The cooperation is studied in terms of the percentage of light coming from the main access point and a parameter called sidelobes??? amplitude level. The performance is evaluated according to the location within the atto-cell.},

keywords = {CoMP, Cooperative transmission andreception, Interference, Journal, Nonlinear optics, Optical receivers, Proposals, Pulse Position Division Multiplexing, Radio frequency, VLC, Wireless communication},

pubstate = {published},

tppubtype = {article}

}

@article{Ramirez2015,

title = {Detection of Multivariate Cyclostationarity},

author = {David Ram\'{i}rez and Peter J Schreier and Javier Via and Ignacio Santamaria and L L Scharf},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7134806},

doi = {10.1109/TSP.2015.2450201},

issn = {1053-587X},

year  = {2015},

date = {2015-10-01},

journal = {IEEE Transactions on Signal Processing},

volume = {63},

number = {20},

pages = {5395--5408},

publisher = {IEEE},

abstract = {This paper derives an asymptotic generalized likelihood ratio test (GLRT) and an asymptotic locally most powerful invariant test (LMPIT) for two hypothesis testing problems: 1) Is a vector-valued random process cyclostationary (CS) or is it wide-sense stationary (WSS)? 2) Is a vector-valued random process CS or is it nonstationary? Our approach uses the relationship between a scalar-valued CS time series and a vector-valued WSS time series for which the knowledge of the cycle period is required. This relationship allows us to formulate the problem as a test for the covariance structure of the observations. The covariance matrix of the observations has a block-Toeplitz structure for CS and WSS processes. By considering the asymptotic case where the covariance matrix becomes block-circulant we are able to derive its maximum likelihood (ML) estimate and thus an asymptotic GLRT. Moreover, using Wijsman's theorem, we also obtain an asymptotic LMPIT. These detectors may be expressed in terms of the Loève spectrum, the cyclic spectrum, and the power spectral density, establishing how to fuse the information in these spectra for an asymptotic GLRT and LMPIT. This goes beyond the state-of-the-art, where it is common practice to build detectors of cyclostationarity from ad-hoc functions of these spectra.},

keywords = {ad hoc function, asymptotic GLRT, asymptotic LMPIT, block circulant, block-Toeplitz structure, Correlation, covariance matrices, Covariance matrix, covariance structure, cycle period, cyclic spectrum, Cyclostationarity, Detectors, Frequency-domain analysis, generalized likelihood ratio test, generalized likelihood ratio test (GLRT), hypothesis testing problem, locally most powerful invariant test, locally most powerful invariant test (LMPIT), Loe{\&}{#}x0300, maximum likelihood estimation, multivariate cyclostationarity detection, power spectral density, random processes, s theorem, scalar valued CS time series, signal detection, spectral analysis, statistical testing, Testing, Time series, Time series analysis, Toeplitz matrices, Toeplitz matrix, ve spectrum, vector valued random process cyclostationary, vector valued WSS time series, wide sense stationary, Wijsman theorem, Wijsman{\&}{#}x2019},

pubstate = {published},

tppubtype = {article}

}

@article{Elvira2015bb,

title = {Efficient Multiple Importance Sampling Estimators},

author = {Victor Elvira and Luca Martino and David Luengo and Monica F Bugallo},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7105865},

doi = {10.1109/LSP.2015.2432078},

issn = {1070-9908},

year  = {2015},

date = {2015-10-01},

journal = {IEEE Signal Processing Letters},

volume = {22},

number = {10},

pages = {1757--1761},

publisher = {IEEE},

abstract = {Multiple importance sampling (MIS) methods use a set of proposal distributions from which samples are drawn. Each sample is then assigned an importance weight that can be obtained according to different strategies. This work is motivated by the trade-off between variance reduction and computational complexity of the different approaches (classical vs. deterministic mixture) available for the weight calculation. A new method that achieves an efficient compromise between both factors is introduced in this letter. It is based on forming a partition of the set of proposal distributions and computing the weights accordingly. Computer simulations show the excellent performance of the associated partial deterministic mixture MIS estimator.},

keywords = {Adaptive importance sampling, classical mixture approach, computational complexity, Computational efficiency, Computer Simulation, deterministic mixture, estimation theory, Journal, Monte Carlo methods, multiple importance sampling, multiple importance sampling estimator, partial deterministic mixture MIS estimator, Proposals, signal sampling, Sociology, Standards, variance reduction, weight calculation},

pubstate = {published},

tppubtype = {article}

}

@article{Mihaljevic2015,

title = {Classifying GABAergic interneurons with semi-supervised projected model-based clustering.},

author = {Bojan Mihaljevi\'{c} and Ruth Benavides-Piccione and Luis Guerra and Javier DeFelipe and Pedro Larra\~{n}aga and Concha Bielza},

url = {http://www.aiimjournal.com/article/S0933365714001481/fulltext http://cig.fi.upm.es/articles/2015/Mihaljevic-2015-AIIM.pdf},

doi = {10.1016/j.artmed.2014.12.010},

issn = {1873-2860},

year  = {2015},

date = {2015-09-01},

journal = {Artificial intelligence in medicine},

volume = {65},

number = {1},

pages = {49--59},

publisher = {Elsevier},

abstract = {OBJECTIVES: A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names. We sought to automatically classify digitally reconstructed interneuronal morphologies according to this scheme. Simultaneously, we sought to discover possible subtypes of these types that might emerge during automatic classification (clustering). We also investigated which morphometric properties were most relevant for this classification. MATERIALS AND METHODS: A set of 118 digitally reconstructed interneuronal morphologies classified into the common basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of the world's leading neuroscientists, quantified by five simple morphometric properties of the axon and four of the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. We then removed this class information for each type separately, and applied semi-supervised clustering to those cells (keeping the others' cluster membership fixed), to assess separation from other types and look for the formation of new groups (subtypes). We performed this same experiment unlabeling the cells of two types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixture of Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performed the described experiments on three different subsets of the data, formed according to how many experts agreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least 26 (47 neurons). RESULTS: Interneurons with more reliable type labels were classified more accurately. We classified HT cells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy, respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, and no subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette width and ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively, confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a single type also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometric properties were more relevant that dendritic ones, with the axonal polar histogram length in the [$pi$, 2$pi$) angle interval being particularly useful. CONCLUSIONS: The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heterogeneous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones for distinguishing among the CB, HT, LB, and MA interneuron types.},

keywords = {Automatic neuron classification, CASI CAM CM, Cerebral cortex, CIG UPM, Gaussian mixture models, Journal, Semi-supervised projected clustering},

pubstate = {published},

tppubtype = {article}

}

@article{Borchani2015,

title = {A survey on multi-output regression},

author = {Hanen Borchani and Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},

url = {http://doi.wiley.com/10.1002/widm.1157 http://cig.fi.upm.es/articles/2015/Borchani-2015-WDMKD.pdf},

doi = {10.1002/widm.1157},

issn = {19424787},

year  = {2015},

date = {2015-09-01},

journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},

volume = {5},

number = {5},

pages = {216--233},

abstract = {In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of multi-output regression. This study provides a survey on state-of-the-art multi-output regression methods, that are categorized as problem transformation and algorithm adaptation methods. In addition, we present the mostly used performance evaluation measures, publicly available data sets for multi-output regression real-world problems, as well as open-source software frameworks.},

keywords = {algorithm adaptation methods, CASI CAM CM, CIG UPM, Journal, Multi-output regression, multi-target regression, performance evaluation measure, problem transformation methods},

pubstate = {published},

tppubtype = {article}

}

@article{Moreno2015b,

title = {Bayesian Nonparametric Crowdsourcing},

author = {Pablo Garcia-Moreno and Yee Whye Teh and Fernando Perez-Cruz and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://www.jmlr.org/papers/volume16/moreno15a/moreno15a.pdf},

year  = {2015},

date = {2015-08-01},

journal = {Journal of Machine Learning Research},

volume = {16},

number = {August},

pages = {1607--1627},

abstract = {Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets. User annotations are often noisy, so methods to combine the annotations to produce reliable estimates of the ground truth are necessary. We claim that considering the existence of clusters of users in this combination step can improve the performance. This is especially important in early stages of crowdsourcing implementations, where the number of annotations is low. At this stage there is not enough information to accurately estimate the bias introduced by each annotator separately, so we have to resort to models that consider the statistical links among them. In addition, finding these clusters is interesting in itself as knowing the behavior of the pool of annotators allows implementing efficient active learning strategies. Based on this, we propose in this paper two new fully unsupervised models based on a Chinese Restaurant Process (CRP) prior and a hierarchical structure that allows inferring these groups jointly with the ground truth and the properties of the users. Efficient inference algorithms based on Gibbs sampling with auxiliary variables are proposed. Finally, we perform experiments, both on synthetic and real databases, to show the advantages of our models over state-of-the-art algorithms.},

keywords = {Bayesian nonparametrics, Dirichlet process, Gibbs sampling, Hierarchical clustering, Journal, Multiple annotators},

pubstate = {published},

tppubtype = {article}

}

@article{Martino2015bbb,

title = {An Adaptive Population Importance Sampler: Learning From Uncertainty},

author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7117437},

doi = {10.1109/TSP.2015.2440215},

issn = {1053-587X},

year  = {2015},

date = {2015-08-01},

journal = {IEEE Transactions on Signal Processing},

volume = {63},

number = {16},

pages = {4422--4437},

publisher = {IEEE},

abstract = {Monte Carlo (MC) methods are well-known computational techniques, widely used in different fields such as signal processing, communications and machine learning. An important class of MC methods is composed of importance sampling (IS) and its adaptive extensions, such as population Monte Carlo (PMC) and adaptive multiple IS (AMIS). In this paper, we introduce a novel adaptive and iterated importance sampler using a population of proposal densities. The proposed algorithm, named adaptive population importance sampling (APIS), provides a global estimation of the variables of interest iteratively, making use of all the samples previously generated. APIS combines a sophisticated scheme to build the IS estimators (based on the deterministic mixture approach) with a simple temporal adaptation (based on epochs). In this way, APIS is able to keep all the advantages of both AMIS and PMC, while minimizing their drawbacks. Furthermore, APIS is easily parallelizable. The cloud of proposals is adapted in such a way that local features of the target density can be better taken into account compared to single global adaptation procedures. The result is a fast, simple, robust, and high-performance algorithm applicable to a wide range of problems. Numerical results show the advantages of the proposed sampling scheme in four synthetic examples and a localization problem in a wireless sensor network.},

keywords = {Adaptive importance sampling, adaptive multiple IS, adaptive population importance sampler, AMIS, APIS, Estimation, Importance sampling, IS estimators, iterative estimation, iterative methods, Journal, MC methods, Monte Carlo (MC) methods, Monte Carlo methods, population Monte Carlo, Proposals, Signal processing algorithms, simple temporal adaptation, Sociology, Standards, Wireless sensor network, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {article}

}

@article{Read2015b,

title = {Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises},

author = {Jesse Read and Luca Martino and Pablo M Olmos and David Luengo},

url = {http://www.sciencedirect.com/science/article/pii/S0031320315000084},

year  = {2015},

date = {2015-06-01},

journal = {Pattern Recognition},

volume = {48},

number = {6},

pages = {2096--2106},

abstract = {Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier chains, in which the predictions of individual classifiers are cascaded along a chain, thus taking into account inter-label dependencies and improving the overall performance. Several varieties of classifier chain methods have been introduced, and many of them perform very competitively across a wide range of benchmark datasets. However, scalability limitations become apparent on larger datasets when modeling a fully cascaded chain. In particular, the methods' strategies for discovering and modeling a good chain structure constitutes a mayor computational bottleneck. In this paper, we present the classifier trellis (CT) method for scalable multi-label classification. We compare CT with several recently proposed classifier chain methods to show that it occupies an important niche: it is highly competitive on standard multi-label problems, yet it can also scale up to thousands or even tens of thousands of labels.},

keywords = {Bayesian networks, classifer chains, Journal, Multi-label classification, multi-output prediction, structured inference},

pubstate = {published},

tppubtype = {article}

}

@article{Varando2015a,

title = {Decision functions for chain classifiers based on Bayesian networks for multi-label classification},

author = {Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},

url = {http://www.researchgate.net/publication/279069321_Decision_functions_for_chain_classifiers_based_on_Bayesian_networks_for_multi-label_classification http://cig.fi.upm.es/node/887},

doi = {10.1016/j.ijar.2015.06.006},

issn = {0888613X},

year  = {2015},

date = {2015-06-01},

journal = {International Journal of Approximate Reasoning},

abstract = {Multi-label classification problems require each instance to be assigned a subset of a defined set of labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of binary classes. In this paper we study the decision boundaries of two widely used approaches for building multi-label classifiers, when Bayesian network-augmented naive Bayes classifiers are used as base models: Binary relevance method and chain classifiers. In particular extending previous single-label results to multi-label chain classifiers, we find polynomial expressions for the multi-valued decision functions associated with these methods. We prove upper boundings on the expressive power of both methods and we prove that chain classifiers provide a more expressive model than the binary relevance method Decision functions for chain classifiers based on Bayesian networks for multi-label classification. Available from: http://www.researchgate.net/publication/279069321_Decision_functions_for_chain_classifiers_based_on_Bayesian_networks_for_multi-label_classification [accessed Nov 15, 2015].},

keywords = {CASI CAM CM, CIG UPM, Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Olmos2015bb,

title = {A Scaling Law to Predict the Finite-Length Performance of Spatially-Coupled LDPC Codes},

author = {Pablo M Olmos and Rudiger Urbanke},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7086074},

doi = {10.1109/TIT.2015.2422816},

issn = {0018-9448},

year  = {2015},

date = {2015-06-01},

journal = {IEEE Transactions on Information Theory},

volume = {61},

number = {6},

pages = {3164--3184},

abstract = {Spatially-coupled low-density parity-check (SC-LDPC) codes are known to have excellent asymptotic properties. Much less is known regarding their finite-length performance. We propose a scaling law to predict the error probability of finite-length spatially coupled code ensembles when transmission takes place over the binary erasure channel. We discuss how the parameters of the scaling law are connected to fundamental quantities appearing in the asymptotic analysis of these ensembles and we verify that the predictions of the scaling law fit well to the data derived from simulations over a wide range of parameters. The ultimate goal of this line of research is to develop analytic tools for the design of SC-LDPC codes under practical constraints.},

keywords = {asymptotic analysis, asymptotic properties, binary erasure channel, Channel Coding, Codes on graphs, Couplings, Decoding, Differential equations, error probability, finite length performance, finite length spatially coupled code, finite-length code performance, finite-length performance, Iterative decoding, iterative decoding thresholds, Journal, parity check codes, Probability, SC-LDPC codes, scaling law, Sockets, spatially coupled LDPC codes, spatially-coupled LDPC codes},

pubstate = {published},

tppubtype = {article}

}

@article{Ruiz2015b,

title = {A Generative Model for Predicting Outcomes in College Basketball},

author = {Francisco J R Ruiz and Fernando Perez-Cruz},

url = {http://www.degruyter.com/view/j/jqas.2015.11.issue-1/jqas-2014-0055/jqas-2014-0055.xml},

doi = {10.1515/jqas-2014-0055},

issn = {1559-0410},

year  = {2015},

date = {2015-03-01},

journal = {Journal of Quantitative Analysis in Sports},

volume = {11},

number = {1 Special Issue},

pages = {39--52},

abstract = {We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.},

keywords = {CASI CAM CM, GAMMA-L+ UC3M, Journal, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference},

pubstate = {published},

tppubtype = {article}

}

@article{Koblents2014b,

title = {A Population Monte Carlo Scheme with Transformed Weights and Its Application to Stochastic Kinetic Models},

author = {Eugenia Koblents and Joaquin Miguez},

url = {http://link.springer.com/10.1007/s11222-013-9440-2 http://gts.tsc.uc3m.es/wp-content/uploads/2014/01/NPMC_A-population-Monte-Carlo-scheme-with-transformed_jma.pdf},

doi = {10.1007/s11222-013-9440-2},

issn = {0960-3174},

year  = {2015},

date = {2015-03-01},

journal = {Statistics and Computing},

volume = {25},

number = {2},

pages = {407--425},

abstract = {This paper addresses the Monte Carlo approximation of posterior probability distributions. In particular, we consider the population Monte Carlo (PMC) technique, which is based on an iterative importance sampling (IS) approach. An important drawback of this methodology is the degeneracy of the importance weights (IWs) when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a new method that performs a nonlinear transformation of the IWs. This operation reduces the weight variation, hence it avoids degeneracy and increases the efficiency of the IS scheme, specially when drawing from proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a simple problem that enables us to discuss the main features of the proposed technique. As a practical application, we have also considered the challenging problem of estimating the rate parameters of a stochastic kinetic model (SKM). SKMs are multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.},

keywords = {COMPREHENSION, degeneracy of importance weights, Importance sampling, Journal, population Monte Carlo, Stochastic kinetic models},

pubstate = {published},

tppubtype = {article}

}

@article{Varando2015b,

title = {Conditional Density Approximations with Mixtures of Polynomials},

author = {Gherardo Varando and Pedro L L\'{o}pez-Cruz and Thomas D Nielsen and Pedro Larra\~{n}aga and Concha Bielza},

url = {http://doi.wiley.com/10.1002/int.21699 http://cig.fi.upm.es/articles/2015/Varando-2015-IJIS.pdf},

doi = {10.1002/int.21699},

issn = {08848173},

year  = {2015},

date = {2015-03-01},

journal = {International Journal of Intelligent Systems},

volume = {30},

number = {3},

pages = {236--264},

abstract = {Mixtures of polynomials (MoPs) are a nonparametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multidimensional (marginal) MoPs from data have recently been proposed. In this paper, we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.},

keywords = {CASI CAM CM, CIG UPM, Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Ruiz2015bb,

title = {A Generative Model for Predicting Outcomes in College Basketball},

author = {Francisco J R Ruiz and Fernando Perez-Cruz},

url = {http://www.degruyter.com/view/j/jqas.2015.11.issue-1/jqas-2014-0055/jqas-2014-0055.xml},

doi = {10.1515/jqas-2014-0055},

issn = {1559-0410},

year  = {2015},

date = {2015-03-01},

journal = {Journal of Quantitative Analysis in Sports},

volume = {11},

number = {1 Special Issue},

pages = {39--52},

abstract = {We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.},

keywords = {CASI CAM CM, GAMMA-L+ UC3M, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference},

pubstate = {published},

tppubtype = {article}

}

@article{Salamanca2014bb,

title = {Approaching the DT Bound Using Linear Codes in the Short Blocklength Regime},

author = {Luis Salamanca and Juan Jos\'{e} Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz and Sergio Verdu},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6957577},

doi = {10.1109/LCOMM.2014.2371032},

issn = {1089-7798},

year  = {2015},

date = {2015-02-01},

journal = {IEEE Communications Letters},

volume = {19},

number = {2},

pages = {123--126},

abstract = {The dependence-testing (DT) bound is one of the strongest achievability bounds for the binary erasure channel (BEC) in the finite block length regime. In this paper, we show that maximum likelihood decoded regular low-density paritycheck (LDPC) codes with at least 5 ones per column almost achieve the DT bound. Specifically, using quasi-regular LDPC codes with block length of 256 bits, we achieve a rate that is less than 1% away from the rate predicted by the DT bound for a word error rate below 103. The results also indicate that the maximum-likelihood solution is computationally feasible for decoding block codes over the BEC with several hundred bits.},

keywords = {binary erasure channel, Channel Coding, Complexity theory, finite blocklength regime, LDPC codes, Maximum likelihood decoding, ML decoding, parity check codes, random coding},

pubstate = {published},

tppubtype = {article}

}

@article{Manzano2015,

title = {Cognitive Self-Scheduled Mechanism for Access Control in Noisy Vehicular Ad Hoc Networks},

author = {Mario Manzano and Felipe Espinosa and \'{A}ngel M Bravo-Santos and Alfredo Gardel-Vicente},

url = {http://www.hindawi.com/journals/mpe/2015/354292/ http://dx.doi.org/10.1155/2015/354292},

doi = {10.1155/2015/354292},

year  = {2015},

date = {2015-01-01},

journal = {Mathematical Problems in Engineering.},

volume = {2015},

pages = {1--12},

abstract = {Within the challenging environment of intelligent transportation systems (ITS), networked control systems such as platooning guidance of autonomous vehicles require innovative mechanisms to provide real-time communications. Although several proposals are currently under discussion, the design of a rapid, efficient, flexible, and reliable medium access control mechanism which meets the specific constraints of such real-time communications applications remains unsolved in this highly dynamic environment. However, cognitive radio (CR) combines the capacity to sense the radio spectrum with the flexibility to adapt to transmission parameters in order to maximize system performance and has thus become an effective approach for the design of dynamic spectrum access (DSA) mechanisms. This paper presents the enhanced noncooperative cognitive division multiple access (ENCCMA) proposal combining time division multiple access (TDMA) and frequency division multiple access (FDMA) schemes with CR techniques to obtain a mechanism fulfilling the requirements of real-time communications. The analysis presented here considers the IEEE WAVE and 802.11p as reference standards; however, the proposed medium access control (MAC) mechanism can be adapted to operate on the physical layer of different standards. The mechanism also offers the advantage of avoiding signaling, thus enhancing system autonomy as well as behavior in adverse scenarios.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Varando2015c,

title = {Decision boundary for discrete Bayesian network classifiers},

author = {Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},

url = {http://cig.fi.upm.es/node/881 http://cig.fi.upm.es/articles/2015/Varando-2015-JMLR.pdf},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Machine Learning Research},

abstract = {Bayesian network classi ers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classi ers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V -structures in the predictor sub-graph, we are also able to prove that this family of polynomials does indeed characterize the speci c classi er considered. We then use this representation to bound the number of decision functions representable by Bayesian network classi ers with a given structure},

keywords = {Bayesian networks, CASI CAM CM, CIG UPM, decision boundary, Journal, Lagrange basis, polynomial, supervised classication, threshold function},

pubstate = {published},

tppubtype = {article}

}

@article{Manzano2015b,

title = {Cognitive Self-Scheduled Mechanism for Access Control in Noisy Vehicular Ad Hoc Networks},

author = {Mario Manzano and Felipe Espinosa and \'{A}ngel M Bravo-Santos and Alfredo Gardel-Vicente},

url = {http://www.hindawi.com/journals/mpe/2015/354292/ http://dx.doi.org/10.1155/2015/354292},

doi = {10.1155/2015/354292},

year  = {2015},

date = {2015-01-01},

journal = {Mathematical Problems in Engineering.},

volume = {2015},

pages = {1--12},

abstract = {Within the challenging environment of intelligent transportation systems (ITS), networked control systems such as platooning guidance of autonomous vehicles require innovative mechanisms to provide real-time communications. Although several proposals are currently under discussion, the design of a rapid, efficient, flexible, and reliable medium access control mechanism which meets the specific constraints of such real-time communications applications remains unsolved in this highly dynamic environment. However, cognitive radio (CR) combines the capacity to sense the radio spectrum with the flexibility to adapt to transmission parameters in order to maximize system performance and has thus become an effective approach for the design of dynamic spectrum access (DSA) mechanisms. This paper presents the enhanced noncooperative cognitive division multiple access (ENCCMA) proposal combining time division multiple access (TDMA) and frequency division multiple access (FDMA) schemes with CR techniques to obtain a mechanism fulfilling the requirements of real-time communications. The analysis presented here considers the IEEE WAVE and 802.11p as reference standards; however, the proposed medium access control (MAC) mechanism can be adapted to operate on the physical layer of different standards. The mechanism also offers the advantage of avoiding signaling, thus enhancing system autonomy as well as behavior in adverse scenarios.},

keywords = {Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Luengo2014bb,

title = {Blind Analysis of Atrial Fibrillation Electrograms: A Sparsity-Aware Formulation},

author = {David Luengo and Sandra Monzon and Tom Trigano and Javier V\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://content.iospress.com/articles/integrated-computer-aided-engineering/ica00471 

http://www.tsc.uc3m.es/~dluengo/sparseEGM.pdf},

year  = {2015},

date = {2015-01-01},

journal = {Integrated Computer-Aided Engineering},

volume = {22},

number = {1},

pages = {71--85},

abstract = {The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation (AF) conditions is considered in this paper. A mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF is firstly introduced. Then, a reconstruction model based on a fixed dictionary is developed and several alternatives for choosing the dictionary are discussed. In order to obtain a sparse solution, which takes into account the biological restrictions of the problem at the same time, the paper proposes using a Least Absolute Shrinkage and Selection Operator (LASSO) regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic signals and applications on real data are provided to validate the proposed approach.},

keywords = {atrial fibrillation, biomedical signal processing},

pubstate = {published},

tppubtype = {article}

}

@article{Martin-Fernandez2015bb,

title = {A Bayesian Method for Model Selection in Environmental Noise Prediction},

author = {L Mart\'{i}n-Fern\'{a}ndez and Diego Ruiz and Antonio Torija and Joaquin Miguez},

url = {http://www.researchgate.net/publication/268213140_A_Bayesian_method_for_model_selection_in_environmental_noise_prediction},

issn = {1726-2135},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Environmental Informatics},

volume = {January 20},

abstract = {Environmental noise prediction and modeling are key factors for addressing a proper planning and management of urban sound environments. In this paper we propose a maximum a posteriori (MAP) method to compare nonlinear state-space models that describe the problem of predicting environmental sound levels. The numerical implementation of this method is based on particle filtering and we use a Markov chain Monte Carlo technique to improve the resampling step. In order to demonstrate the validity of the proposed approach for this particular problem, we have conducted a set of experiments where two prediction models are quantitatively compared using real noise measurement data collected in different urban areas.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bravo-Santos2014b,

title = {Detectors for Cooperative Mesh Networks with Decode-and-Forward Relays},

author = {\'{A}ngel M Bravo-Santos and Petar M Djuric},

url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6928514},

doi = {10.1109/TSP.2014.2364016},

issn = {1053-587X},

year  = {2015},

date = {2015-01-01},

journal = {IEEE Transactions on Signal Processing},

volume = {63},

number = {1},

pages = {5--17},

publisher = {IEEE},

abstract = {We consider mesh networks composed of groups of relaying nodes which operate in decode-andforward mode. Each node from a group relays information to all the nodes in the next group. We study these networks in two setups, one where the nodes have complete state information about the channels through which they receive the signals, and another when they only have the statistics of the channels. We derive recursive expressions for the probabilities of errors of the nodes and present several implementations of detectors used in these networks. We compare the mesh networks with multi-hop networks formed by a set of parallel sections of multiple relaying nodes. We demonstrate with numerous simulations that there are significant improvements in performance of mesh over multi-hop networks in various scenarios.},

keywords = {Cooperative systems, Detectors, Mesh networks, Modulation, Relays, spread spectrum communication, Wireless communication},

pubstate = {published},

tppubtype = {article}

}

@article{Bravo-Santos2014bb,

title = {Detectors for Cooperative Mesh Networks with Decode-and-Forward Relays},

author = {\'{A}ngel M Bravo-Santos and Petar M Djuric},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6928514},

doi = {10.1109/TSP.2014.2364016},

issn = {1053-587X},

year  = {2015},

date = {2015-01-01},

journal = {IEEE Transactions on Signal Processing},

volume = {63},

number = {1},

pages = {5--17},

publisher = {IEEE},

abstract = {We consider mesh networks composed of groups of relaying nodes which operate in decode-andforward mode. Each node from a group relays information to all the nodes in the next group. We study these networks in two setups, one where the nodes have complete state information about the channels through which they receive the signals, and another when they only have the statistics of the channels. We derive recursive expressions for the probabilities of errors of the nodes and present several implementations of detectors used in these networks. We compare the mesh networks with multi-hop networks formed by a set of parallel sections of multiple relaying nodes. We demonstrate with numerous simulations that there are significant improvements in performance of mesh over multi-hop networks in various scenarios.},

keywords = {Cooperative systems, Detectors, Journal, Mesh networks, Modulation, Relays, spread spectrum communication, Wireless communication},

pubstate = {published},

tppubtype = {article}

}

@article{Santiago-Mozos2013,

title = {An Automated Screening System for Tuberculosis},

author = {Ricardo Santiago-Mozos and Fernando Perez-Cruz and Michael Madden and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://www.tsc.uc3m.es/~antonio/papers/P47_2014_An Automated Screening System for Tuberculosis.pdf http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6630069},

issn = {2168-2208},

year  = {2014},

date = {2014-05-01},

journal = {IEEE journal of biomedical and health informatics},

volume = {18},

number = {3},

pages = {855-862},

publisher = {IEEE},

abstract = {Automated screening systems are commonly used to detect some agent in a sample and take a global decision about the subject (e.g. ill/healthy) based on these detections. We propose a Bayesian methodology for taking decisions in (sequential) screening systems that considers the false alarm rate of the detector. Our approach assesses the quality of its decisions and provides lower bounds on the achievable performance of the screening system from the training data. In addition, we develop a complete screening system for sputum smears in tuberculosis diagnosis, and show, using a real-world database, the advantages of the proposed framework when compared to the commonly used count detections and threshold approach.},

keywords = {Automated screening, Bayesian, Decision making, Sequential analysis, Tuberculosis},

pubstate = {published},

tppubtype = {article}

}

@article{Impedovo2014b,

title = {Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains},

author = {Sebastiano Impedovo and Cheng-Lin Liu and Donato Impedovo and Giuseppe Pirlo and Jesse Read and Luca Martino and David Luengo},

url = {http://www.sciencedirect.com/science/article/pii/S0031320313004160},

year  = {2014},

date = {2014-01-01},

journal = {Pattern Recognition},

volume = {47},

number = {3},

pages = {1535--1546},

abstract = {Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance \textendash at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.},

keywords = {Bayesian inference, Classifier chains, Monte Carlo methods, Multi-dimensional classification, Multi-label classification},

pubstate = {published},

tppubtype = {article}

}

@article{Read2014b,

title = {A Distributed Particle Filter for Nonlinear Tracking in Wireless Sensor Networks},

author = {Jesse Read and Katrin Achutegui and Joaquin Miguez},

url = {http://www.tsc.uc3m.es/~jmiguez/papers/P40_2014_A Distributed Particle Filter for Nonlinear Tracking in Wireless Sensor Networks.pdf 

http://www.sciencedirect.com/science/article/pii/S0165168413004568},

year  = {2014},

date = {2014-01-01},

journal = {Signal Processing},

volume = {98},

pages = {121--134},

abstract = {The use of distributed particle filters for tracking in sensor networks has become popular in recent years. The distributed particle filters proposed in the literature up to now are only approximations of the centralized particle filter or, if they are a proper distributed version of the particle filter, their implementation in a wireless sensor network demands a prohibitive communication capability. In this work, we propose a mathematically sound distributed particle filter for tracking in a real-world indoor wireless sensor network composed of low-power nodes. We provide formal and general descriptions of our methodology and then present the results of both real-world experiments and/or computer simulations that use models fitted with real data. With the same number of particles as a centralized filter, the distributed algorithm is over four times faster, yet our simulations show that, even assuming the same processing speed, the accuracy of the centralized and distributed algorithms is practically identical. The main limitation of the proposed scheme is the need to make all the sensor observations available to every processing node. Therefore, it is better suited to broadcast networks or multihop networks where the volume of generated data is kept low, e.g., by an adequate local pre-processing of the observations.},

keywords = {Distributed filtering, Target tracking, Wireless sensor network},

pubstate = {published},

tppubtype = {article}

}

@article{Alvarado2014,

title = {High-SNR Asymptotics of Mutual Information for Discrete Constellations With Applications to BICM},

author = {Alex Alvarado and Fredrik Brannstrom and Erik Agrell and Tobias Koch},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6671479 

http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60%282%29.pdf},

issn = {0018-9448},

year  = {2014},

date = {2014-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {60},

number = {2},

pages = {1061--1076},

abstract = {Asymptotic expressions of the mutual information between any discrete input and the corresponding output of the scalar additive white Gaussian noise channel are presented in the limit as the signal-to-noise ratio (SNR) tends to infinity. Asymptotic expressions of the symbol-error probability (SEP) and the minimum mean-square error (MMSE) achieved by estimating the channel input given the channel output are also developed. It is shown that for any input distribution, the conditional entropy of the channel input given the output, MMSE, and SEP have an asymptotic behavior proportional to the Gaussian Q-function. The argument of the Q-function depends only on the minimum Euclidean distance (MED) of the constellation and the SNR, and the proportionality constants are functions of the MED and the probabilities of the pairs of constellation points at MED. The developed expressions are then generalized to study the high-SNR behavior of the generalized mutual information (GMI) for bit-interleaved coded modulation (BICM). By means of these asymptotic expressions, the long-standing conjecture that Gray codes are the binary labelings that maximize the BICM-GMI at high SNR is proven. It is further shown that for any equally spaced constellation whose size is a power of two, there always exists an anti-Gray code giving the lowest BICM-GMI at high SNR.},

keywords = {additive white Gaussian noise channel, Anti-Gray code, bit-interleaved coded modulation, discrete constellations, Entropy, Gray code, high-SNR asymptotics, IP networks, Labeling, minimum-mean square error, Modulation, Mutual information, Signal to noise ratio, Vectors},

pubstate = {published},

tppubtype = {article}

}

@article{Martin-Fernandez2014,

title = {A Rao-Blackwellized Particle Filter for Joint Parameter Estimation and Biomass Tracking in a Stochastic Predator-Prey System},

author = {L Martin-Fernandez and G Gilioli and E Lanzarone and Joaquin Miguez and S Pasquali and F Ruggeri and D P Ruiz},

url = {http://www.tsc.uc3m.es/~jmiguez/papers/P42_2014_A Rao-Blackwellized Particle Filter for Joint Parameter Estimation and Biomass Tracking in a Stochastic Predator-Prey System.pdf https://www.aimsciences.org/journals/pdfs.jsp?paperID=9557\&mode=full http://gts.tsc.uc3m.es/wp-content/uploads/2014/01/LMF_et_al_MBE13_A-RAO-BLACKWELLIZED-PARTICLE-FILTER_-jma.pdf https://www.aimsciences.org/journals/displayArticlesnew.jsp?paperID=9557},

year  = {2014},

date = {2014-01-01},

journal = {Mathematical Biosciences and Engineering},

volume = {11},

number = {3},

pages = {573--597},

abstract = {Functional response estimation and population tracking in predator- prey systems are critical problems in ecology. In this paper we consider a stochastic predator-prey system with a Lotka-Volterra functional response and propose a particle ltering method for: (a) estimating the behavioral parameter representing the rate of e ective search per predator in the functional response and (b) forecasting the population biomass using eld data. In particular, the proposed technique combines a sequential Monte Carlo sampling scheme for tracking the time-varying biomass with the analytical integration of the unknown behavioral parameter. In order to assess the performance of the method, we show results for both synthetic and observed data collected in an acarine predator-prey system, namely the pest mite Tetranychus urticae and the predatory mite Phytoseiulus persimilis.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pineiro-Ave2014,

title = {Target Detection for Low Cost Uncooled MWIR Cameras Based on Empirical Mode Decomposition},

author = {Jos\'{e} Pi\~{n}eiro-Ave and Manuel Blanco-Velasco and Fernando Cruz-Rold\'{a}n and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://www.tsc.uc3m.es/~antonio/papers/P49_2014_Target Detection for Low Cost Uncooled MWIR Cameras Based on Empirical Mode Decomposition.pdf 

http://www.sciencedirect.com/science/article/pii/S1350449514000085},

issn = {13504495},

year  = {2014},

date = {2014-01-01},

journal = {Infrared Physics \& Technology},

volume = {63},

pages = {222--231},

abstract = {In this work, a novel method for detecting low intensity fast moving objects with low cost Medium Wavelength Infrared (MWIR) cameras is proposed. The method is based on background subtraction in a video sequence obtained with a low density Focal Plane Array (FPA) of the newly available uncooled lead selenide (PbSe) detectors. Thermal instability along with the lack of specific electronics and mechanical devices for canceling the effect of distortion make background image identification very difficult. As a result, the identification of targets is performed in low signal to noise ratio (SNR) conditions, which may considerably restrict the sensitivity of the detection algorithm. These problems are addressed in this work by means of a new technique based on the empirical mode decomposition, which accomplishes drift estimation and target detection. Given that background estimation is the most important stage for detecting, a previous denoising step enabling a better drift estimation is designed. Comparisons are conducted against a denoising technique based on the wavelet transform and also with traditional drift estimation methods such as Kalman filtering and running average. The results reported by the simulations show that the proposed scheme has superior performance.},

keywords = {Background subtraction, Change detection, Denoising, Drift, Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF)},

pubstate = {published},

tppubtype = {article}

}

@article{Koblents2014bb,

title = {A Population Monte Carlo Scheme with Transformed Weights and Its Application to Stochastic Kinetic Models},

author = {Eugenia Koblents and Joaquin Miguez},

url = {http://link.springer.com/10.1007/s11222-013-9440-2 http://gts.tsc.uc3m.es/wp-content/uploads/2014/01/NPMC_A-population-Monte-Carlo-scheme-with-transformed_jma.pdf},

issn = {0960-3174},

year  = {2014},

date = {2014-01-01},

journal = {Statistics and Computing},

number = {(to appear)},

abstract = {This paper addresses the Monte Carlo approximation of posterior probability distributions. In particular, we consider the population Monte Carlo (PMC) technique, which is based on an iterative importance sampling (IS) approach. An important drawback of this methodology is the degeneracy of the importance weights (IWs) when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a new method that performs a nonlinear transformation of the IWs. This operation reduces the weight variation, hence it avoids degeneracy and increases the efficiency of the IS scheme, specially when drawing from proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a simple problem that enables us to discuss the main features of the proposed technique. As a practical application, we have also considered the challenging problem of estimating the rate parameters of a stochastic kinetic model (SKM). SKMs are multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.},

keywords = {degeneracy of importance weights, Importance sampling, population Monte Carlo, Stochastic kinetic models},

pubstate = {published},

tppubtype = {article}

}

@article{Crisan2014bb,

title = {Particle-Kernel Estimation of the Filter Density in State-Space Models},

author = {Dan Crisan and Joaquin Miguez},

url = {http://www.tsc.uc3m.es/~jmiguez/papers/P43_2014_Particle-Kernel Estimation of the Filter Density in State-Space Models.pdf 

http://www.bernoulli-society.org/index.php/publications/bernoulli-journal/bernoulli-journal-papers},

year  = {2014},

date = {2014-01-01},

journal = {Bernoulli},

volume = {(to appear},

abstract = {Sequential Monte Carlo (SMC) methods, also known as particle filters, are simulation-based recursive algorithms for the approximation of the a posteriori probability measures generated by state-space dynamical models. At any given time t, a SMC method produces a set of samples over the state space of the system of interest (often termed “particles”) that is used to build a discrete and random approximation of the posterior probability distribution of the state variables, conditional on a sequence of available observations. One potential application of the methodology is the estimation of the densities associated to the sequence of a posteriori distributions. While practitioners have rather freely applied such density approximations in the past, the issue has received less attention from a theoretical perspective. In this paper, we address the problem of constructing kernel-based estimates of the posterior probability density function and its derivatives, and obtain asymptotic convergence results for the estimation errors. In particular, we find convergence rates for the approximation errors that hold uniformly on the state space and guarantee that the error vanishes almost surely as the number of particles in the filter grows. Based on this uniform convergence result, we first show how to build continuous measures that converge almost surely (with known rate) toward the posterior measure and then address a few applications. The latter include maximum a posteriori estimation of the system state using the approximate derivatives of the posterior density and the approximation of functionals of it, e.g., Shannon’s entropy.},

keywords = {density estimation, Markov systems., Models, Sequential Monte Carlo, state-space, stochastic filtering},

pubstate = {published},

tppubtype = {article}

}

@article{Ruiz2014,

title = {Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders},

author = {Francisco J R Ruiz and Isabel Valera and Carlos Blanco and Fernando Perez-Cruz},

url = {http://jmlr.org/papers/volume15/ruiz14a/ruiz14a.pdf 

http://arxiv.org/abs/1401.7620},

year  = {2014},

date = {2014-01-01},

journal = {Journal of Machine Learning Research},

volume = {15},

number = {1},

pages = {1215--1248},

abstract = {The analysis of comorbidity is an open and complex research field in the branch of psychiatry, where clinical experience and several studies suggest that the relation among the psychiatric disorders may have etiological and treatment implications. In this paper, we are interested in applying latent feature modeling to find the latent structure behind the psychiatric disorders that can help to examine and explain the relationships among them. To this end, we use the large amount of information collected in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database and propose to model these data using a nonparametric latent model based on the Indian Buffet Process (IBP). Due to the discrete nature of the data, we first need to adapt the observation model for discrete random variables. We propose a generative model in which the observations are drawn from a multinomial-logit distribution given the IBP matrix. The implementation of an efficient Gibbs sampler is accomplished using the Laplace approximation, which allows integrating out the weighting factors of the multinomial-logit likelihood model. We also provide a variational inference algorithm for this model, which provides a complementary (and less expensive in terms of computational complexity) alternative to the Gibbs sampler allowing us to deal with a larger number of data. Finally, we use the model to analyze comorbidity among the psychiatric disorders diagnosed by experts from the NESARC database.},

keywords = {ALCIT, Bayesian Non-parametrics, categorical observations, Indian Buet Process, Laplace approximation, multinomial-logit function, variational inference},

pubstate = {published},

tppubtype = {article}

}

@article{O'Mahony2014,

title = {Objective diagnosis of ADHD using IMUs},

author = {Niamh O'Mahony and Blanca Florentino-Lia\~{n}o and Juan J Carballo and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://www.tsc.uc3m.es/~antonio/papers/P50_2014_Objective Diagnosis of ADHD Using IMUs.pdf 

http://www.sciencedirect.com/science/article/pii/S1350453314000459},

issn = {1873-4030},

year  = {2014},

date = {2014-01-01},

journal = {Medical engineering \& physics},

volume = {36},

number = {7},

pages = {922--6},

abstract = {This work proposes the use of miniature wireless inertial sensors as an objective tool for the diagnosis of ADHD. The sensors, consisting of both accelerometers and gyroscopes to measure linear and rotational movement, respectively, are used to characterize the motion of subjects in the setting of a psychiatric consultancy. A support vector machine is used to classify a group of subjects as either ADHD or non-ADHD and a classification accuracy of greater than 95% has been achieved. Separate analyses of the motion data recorded during various activities throughout the visit to the psychiatric consultancy show that motion recorded during a continuous performance test (a forced concentration task) provides a better classification performance than that recorded during "free time".},

keywords = {Attention deficit/hyperactivity disorder, Classification, Inertial sensors, Machine learning, Objective diagnosis},

pubstate = {published},

tppubtype = {article}

}

@article{Montoya-Martinez2014b,

title = {A Regularized Matrix Factorization Approach to Induce Structured Sparse-Low Rank Solutions in the EEG Inverse Problem},

author = {Jair Montoya-Martinez and Antonio Art\'{e}s-Rodr\'{i}guez and Massimiliano Pontil and Lars Kai Hansen},

url = {http://www.tsc.uc3m.es/~antonio/papers/P48_2014_A Regularized Matrix Factorization Approach to Induce Structured Sparse-Low Rank Solutions in the EEG Inverse Problem.pdf 

http://asp.eurasipjournals.com/content/2014/1/97/abstract},

issn = {1687-6180},

year  = {2014},

date = {2014-01-01},

journal = {EURASIP Journal on Advances in Signal Processing},

volume = {2014},

number = {1},

pages = {97},

publisher = {Springer},

abstract = {We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy Electroencephalographic (EEG) measurements, commonly named as the EEG inverse problem. We propose a new method to induce neurophysiological meaningful solutions, which takes into account the smoothness, structured sparsity and low rank of the BES matrix. The method is based on the factorization of the BES matrix as a product of a sparse coding matrix and a dense latent source matrix. The structured sparse-low rank structure is enforced by minimizing a regularized functional that includes the l21-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We analyze the convergence of the optimization procedure, and we compare, under different synthetic scenarios, the performance of our method respect to the Group Lasso and Trace Norm regularizers when they are applied directly to the target matrix.},

keywords = {Low rank, Matrix factorization, Nonsmooth-nonconvex optimization, Regularization, Structured sparsity},

pubstate = {published},

tppubtype = {article}

}

@article{Pastore2014a,

title = {A Rate-Splitting Approach to Fading Channels With Imperfect Channel-State Information},

author = {Pastore A and Tobias Koch and Javier Rodriguez Fonollosa},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6832779 http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60(7).pdf http://arxiv.org/pdf/1301.6120.pdf},

issn = {0018-9448},

year  = {2014},

date = {2014-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {60},

number = {7},

pages = {4266--4285},

publisher = {IEEE},

abstract = {As shown by M\'{e}dard, the capacity of fading channels with imperfect channel-state information can be lower-bounded by assuming a Gaussian channel input (X) with power (P) and by upper-bounding the conditional entropy (h(X|Y,hat {H})) by the entropy of a Gaussian random variable with variance equal to the linear minimum mean-square error in estimating (X) from ((Y,hat {H})) . We demonstrate that, using a rate-splitting approach, this lower bound can be sharpened: by expressing the Gaussian input (X) as the sum of two independent Gaussian variables (X_1) and (X_2) and by applying M\'{e}dard's lower bound first to bound the mutual information between (X_1) and (Y) while treating (X_2) as noise, and by applying it a second time to the mutual information between (X_2) and (Y) while assuming (X_1) to be known, we obtain a capacity lower bound that is strictly larger than M\'{e}dard's lower bound. We then generalize this approach to an arbi- rary number (L) of layers, where (X) is expressed as the sum of (L) independent Gaussian random variables of respective variances (P_ell ) , (ell = 1,dotsc ,L) summing up to (P) . Among all such rate-splitting bounds, we determine the supremum over power allocations (P_ell ) and total number of layers (L) . This supremum is achieved for (L rightarrow infty ) and gives rise to an analytically expressible capacity lower bound. For Gaussian fading, this novel bound is shown to converge to the Gaussian-input mutual information as the signal-to-noise ratio (SNR) grows, provided that the variance of the channel estimation error (H-hat {H}) tends to zero as the SNR tends to infinity.},

keywords = {channel capacity, COMONSENS, DEIPRO, Entropy, Fading, fading channels, flat fading, imperfect channel-state information, MobileNET, Mutual information, OTOSiS, Random variables, Receivers, Signal to noise ratio, Upper bound},

pubstate = {published},

tppubtype = {article}

}

@article{TausteCampo2014,

title = {A Derivation of the Source-Channel Error Exponent Using Nonidentical Product Distributions},

author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guill\'{e}n i F\`{a}bregas and Tobias Koch and Alfonso Martinez},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6803047 http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60(6).pdf},

issn = {0018-9448},

year  = {2014},

date = {2014-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {60},

number = {6},

pages = {3209--3217},

publisher = {IEEE},

abstract = {This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless systems, two optimally chosen classes and product distributions are found to be sufficient to attain the sphere-packing exponent in those cases where it is tight.},

keywords = {ALCIT, Channel Coding, COMONSENS, DEIPRO, error probability, joint source-channel coding, Joints, MobileNET, Probability distribution, product distributions, random coding, Reliability, reliability function, sphere-packing bound, Upper bound},

pubstate = {published},

tppubtype = {article}

}

@article{Cespedes2014,

title = {Expectation Propagation Detection for High-order High-dimensional MIMO Systems},

author = {Javier Cespedes and Pablo M Olmos and Matilde Sanchez-Fernandez and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6841617},

issn = {0090-6778},

year  = {2014},

date = {2014-01-01},

journal = {IEEE Transactions on Communications},

volume = {PP},

number = {99},

pages = {1--1},

abstract = {Modern communications systems use multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the number of antennas and the order of the constellation grow, the design of efficient and low-complexity MIMO receivers possesses big technical challenges. For example, symbol detection can no longer rely on maximum likelihood detection or sphere-decoding methods, as their complexity increases exponentially with the number of transmitters/receivers. In this paper, we propose a low-complexity high-accuracy MIMO symbol detector based on the Expectation Propagation (EP) algorithm. EP allows approximating iteratively at polynomial-time the posterior distribution of the transmitted symbols. We also show that our EP MIMO detector outperforms classic and state-of-the-art solutions reducing the symbol error rate at a reduced computational complexity.},

keywords = {Approximation methods, computational complexity, Detectors, MIMO, Signal to noise ratio, Vectors},

pubstate = {published},

tppubtype = {article}

}

@article{Read2014bb,

title = {Multi-Dimensional Classification with Super-Classes},

author = {Jesse Read and Concha Bielza and Pedro Larranaga},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6648319},

issn = {1041-4347},

year  = {2014},

date = {2014-01-01},

journal = {IEEE Transactions on Knowledge and Data Engineering},

volume = {26},

number = {7},

pages = {1720--1733},

publisher = {IEEE},

abstract = {The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.},

keywords = {Accuracy, Bayes methods, Classification, COMPRHENSION, conditional dependence, Context, core goals, data instance, evaluation metrics, Integrated circuit modeling, modeling class dependencies, multi-dimensional, Multi-dimensional classification, multidimensional classification problem, multidimensional datasets, multidimensional learners, multilabel classification, multilabel research, multiple class variables, ordinary class, pattern classification, problem transformation, recently-popularized task, super classes, super-class partitions, tractable running time, Training, Vectors},

pubstate = {published},

tppubtype = {article}

}

@article{Salamanca2014bb,

title = {Near DT Bound Achieving Linear Codes in the Short Blocklength Regime},

author = {Luis Salamanca and Juan Jos\'{e} Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz and Sergio Verdu},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6957577},

issn = {1089-7798},

year  = {2014},

date = {2014-01-01},

journal = {IEEE Communications Letters},

volume = {PP},

number = {99},

pages = {1--1},

abstract = {The dependence-testing (DT) bound is one of the strongest achievability bounds for the binary erasure channel (BEC) in the finite block length regime. In this paper, we show that maximum likelihood decoded regular low-density paritycheck (LDPC) codes with at least 5 ones per column almost achieve the DT bound. Specifically, using quasi-regular LDPC codes with block length of 256 bits, we achieve a rate that is less than 1% away from the rate predicted by the DT bound for a word error rate below 103. The results also indicate that the maximum-likelihood solution is computationally feasible for decoding block codes over the BEC with several hundred bits.},

keywords = {binary erasure channel, Channel Coding, Complexity theory, finite blocklength regime, LDPC codes, Maximum likelihood decoding, ML decoding, parity check codes, random coding},

pubstate = {published},

tppubtype = {article}

}

@article{GilTaborda2014,

title = {Information--Estimation Relationships over Binomial and Negative Binomial Models},

author = {Camilo G Taborda and Dongning Guo and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6746122},

issn = {0018-9448},

year  = {2014},

date = {2014-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {to appear},

pages = {1--1},

publisher = {IEEE},

abstract = {In recent years, a number of new connections between information measures and estimation have been found under various models, including, predominantly, Gaussian and Poisson models. This paper develops similar results for the binomial and negative binomial models. In particular, it is shown that the derivative of the relative entropy and the derivative of the mutual information for the binomial and negative binomial models can be expressed through the expectation of closed-form expressions that have conditional estimates as the main argument. Under mild conditions, those derivatives take the form of an expected Bregman divergence},

keywords = {ALCIT},

pubstate = {published},

tppubtype = {article}

}

@article{Yang2014bb,

title = {Quasi-Static Multiple-Antenna Fading Channels at Finite Blocklength},

author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6802432 http://arxiv.org/abs/1311.2012},

issn = {0018-9448},

year  = {2014},

date = {2014-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {60},

number = {7},

pages = {4232--4265},

publisher = {IEEE},

abstract = {This paper investigates the maximal achievable rate for a given blocklength and error probability over quasi-static multiple-input multiple-output fading channels, with and without channel state information at the transmitter and/or the receiver. The principal finding is that outage capacity, despite being an asymptotic quantity, is a sharp proxy for the finite-blocklength fundamental limits of slow-fading channels. Specifically, the channel dispersion is shown to be zero regardless of whether the fading realizations are available at both transmitter and receiver, at only one of them, or at neither of them. These results follow from analytically tractable converse and achievability bounds. Numerical evaluation of these bounds verifies that zero dispersion may indeed imply fast convergence to the outage capacity as the blocklength increases. In the example of a particular 1 $,times,$ 2 single-input multiple-output Rician fading channel, the blocklength required to achieve 90% of capacity is about an order of magnitude smaller compared with the blocklength required for an AWGN channel with the same capacity. For this specific scenario, the coding/decoding schemes adopted in the LTE-Advanced standard are benchmarked against the finite-blocklength achievability and converse bounds.},

keywords = {channel dispersion, Decoding, error probability, finite blocklength regime, MIMO, MIMO channel, outage probability, quasi-static fading channel, Rayleigh channels, Receivers, Transmitters},

pubstate = {published},

tppubtype = {article}

}

@article{Olmos2013b,

title = {Tree-Structure Expectation Propagation for LDPC Decoding Over the BEC},

author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6451276},

issn = {0018-9448},

year  = {2013},

date = {2013-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {59},

number = {6},

pages = {3354--3377},

abstract = {We present the tree-structure expectation propagation (Tree-EP) algorithm to decode low-density parity-check (LDPC) codes over discrete memoryless channels (DMCs). Expectation propagation generalizes belief propagation (BP) in two ways. First, it can be used with any exponential family distribution over the cliques in the graph. Second, it can impose additional constraints on the marginal distributions. We use this second property to impose pairwise marginal constraints over pairs of variables connected to a check node of the LDPC code's Tanner graph. Thanks to these additional constraints, the Tree-EP marginal estimates for each variable in the graph are more accurate than those provided by BP. We also reformulate the Tree-EP algorithm for the binary erasure channel (BEC) as a peeling-type algorithm (TEP) and we show that the algorithm has the same computational complexity as BP and it decodes a higher fraction of errors. We describe the TEP decoding process by a set of differential equations that represents the expected residual graph evolution as a function of the code parameters. The solution of these equations is used to predict the TEP decoder performance in both the asymptotic regime and the finite-length regimes over the BEC. While the asymptotic threshold of the TEP decoder is the same as the BP decoder for regular and optimized codes, we propose a scaling law for finite-length LDPC codes, which accurately approximates the TEP improved performance and facilitates its optimization.},

keywords = {Algorithm design and analysis, Approximation algorithms, Approximation methods, BEC, belief propagation, Belief-propagation (BP), binary erasure channel, Complexity theory, decode low-density parity-check codes, Decoding, discrete memoryless channels, expectation propagation, finite-length analysis, LDPC codes, LDPC decoding, parity check codes, peeling-type algorithm, Probability density function, random graph evolution, Tanner graph, tree-structure expectation propagation},

pubstate = {published},

tppubtype = {article}

}

@article{Asheghan2013,

title = {Robust Global Synchronization of two Complex Dynamical Networks},

author = {Mohammad Mostafa Asheghan and Joaquin Miguez},

url = {http://www.tsc.uc3m.es/~jmiguez/papers/P38_2013_Robust Global Synchronization of two Complex Dynamical Networks.pdf 

http://www.researchgate.net/publication/245026922_Robust_global_synchronization_of_two_complex_dynamical_networks},

issn = {1089-7682},

year  = {2013},

date = {2013-01-01},

journal = {Chaos (Woodbury, N.Y.)},

volume = {23},

number = {2},

pages = {023108},

abstract = {We investigate the synchronization of two coupled complex dynamical networks, a problem that has been termed outer synchronization in the literature. Our approach relies on (a) a basic lemma on the eigendecomposition of matrices resulting from Kronecker products and (b) a suitable choice of Lyapunov function related to the synchronization error dynamics. Starting from these two ingredients, a theorem that provides a sufficient condition for outer synchronization of the networks is proved. The condition in the theorem is expressed as a linear matrix inequality. When satisfied, synchronization is guaranteed to occur globally, i.e., independently of the initial conditions of the networks. The argument of the proof includes the design of the gain of the synchronizer, which is a constant square matrix with dimension dependent on the number of dynamic variables in a single network node, but independent of the size of the overall network, which can be much larger. This basic result is subsequently elaborated to simplify the design of the synchronizer, to avoid unnecessarily restrictive assumptions (e.g., diffusivity) on the coupling matrix that defines the topology of the networks and, finally, to obtain synchronizers that are robust to model errors in the parameters of the coupled networks. An illustrative numerical example for the outer synchronization of two networks of classical Lorenz nodes with perturbed parameters is presented.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jingshan2013,

title = {Sparse ACEKF for Phase Reconstruction.},

author = {Zhong Jingshan and Justin Dauwels and Manuel A Vazquez and Laura Waller},

url = {http://www.opticsinfobase.org/viewmedia.cfm?uri=oe-21-15-18125\&seq=0\&html=true},

issn = {1094-4087},

year  = {2013},

date = {2013-01-01},

journal = {Optics express},

volume = {21},

number = {15},

pages = {18125--37},

publisher = {Optical Society of America},

abstract = {We propose a novel low-complexity recursive filter to efficiently recover quantitative phase from a series of noisy intensity images taken through focus. We first transform the wave propagation equation and nonlinear observation model (intensity measurement) into a complex augmented state space model. From the state space model, we derive a sparse augmented complex extended Kalman filter (ACEKF) to infer the complex optical field (amplitude and phase), and find that it converges under mild conditions. Our proposed method has a computational complexity of N(z)N logN and storage requirement of O(N), compared with the original ACEKF method, which has a computational complexity of O(NzN(3)) and storage requirement of O(N(2)), where Nz is the number of images and N is the number of pixels in each image. Thus, it is efficient, robust and recursive, and may be feasible for real-time phase recovery applications with high resolution images.},

keywords = {Image reconstruction techniques, Phase retrieval},

pubstate = {published},

tppubtype = {article}

}