A class of diffusion proportionate subband adaptive filters for sparse system identification over distributed networks

This paper aims to extend the proportionate adaptation concept to the design of a class of diffusion normalized subband adaptive filter (DNSAF) algorithms. This leads to four extensions of the algorithm associated with different step-size variations, namely diffusion proportionate normalized subband adaptive filter (DPNSAF), diffusion μ-law PNSAF (DMPNSAF), diffusion improved PNSAF (DIPNSAF) and diffusion improved IPNSAF (DIIPNSAF). Subsequently, steady-state performance, stability conditions and computational complexity of the proposed algorithms are investigated. For each extension the performance has been evaluated using both real and simulated data, where the outcomes demonstrate the accuracy of the theoretical expressions and effectiveness of the proposed algorithms

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Modelling collective movement across scales: from cells to wildebeest

Collective movements are ubiquitous in biological systems, occurring at all scales; from the sub-organismal movements of groups of cells, to the far-ranging movements of bird flocks and herds of large herbivores. Movement patterns at these vastly different scales often exhibit surprisingly similar patterns, suggesting that mathematically similar mechanisms may drive collective movements across many systems. The aims of this study were three-fold. First, to develop mechanistic movement models capable of producing the observed wealth of spatial patterns. Second, to tailor statistical inference approaches to these models that are capable of identifying drivers of collective movement that could be applied to a wide range of study systems. Third, to validate the approaches by fitting the mechanistic models to data from diverse biological systems. These study systems included two small-scale in vitro cellular systems, involving movement of groups of human melanoma cells and Dictyostelium discoideum (slime mould) cells, and a third much larger-scale system, involving wildebeest in the Serengeti ecosystem. I developed a series of mechanistic movement models, based on advection-diffusion partial differential equations and integro-differential equations, that describe changes in the spatio-temporal distribution of the study population as a consequence of various movement drivers, including environmental gradients, environmental depletion, social behaviour, and spatial and temporal heterogeneity in the response of the individuals to these drivers. I also developed a number of approaches to statistical inference (comprising both parameter estimation and model comparison) for these models that ranged from frequentist, to pseudo-Bayesian, to fully Bayesian. These inference approaches also varied in whether they required numerical solutions of the models, or whether the need for numerical solutions was bypassed by using gradient matching methods. The inference methods were specifically designed to be effective in the face of the many difficulties presented by advection-diffusion models, particularly high computational costs and instabilities in numerical model solutions, which have previously prevented these models from being fitted to data. It was also necessary for these inference methods to be able to cope with data of different qualities; the cellular data provided accurate information on the locations of all individuals through time, while the wildebeest data consisted of coarse ordinal abundance categories on a spatial grid at monthly intervals. By applying the developed models and inference methods to data from each study system, I drew a number of conclusions about the mechanisms driving movement in these systems. In all three systems, for example, there was evidence of a saturating response to an environmental gradient in a resource or chemical attractant that the individuals could deplete locally. I also found evidence of temporal dependence in the movement parameters for all systems. This indicates that the simplifying assumption that behaviour is constant, which has been made by many previous studies that have modelled movement, is unlikely to be justified. Differences between the systems were also demonstrated, such as overcrowding affecting the movements of melanoma and wildebeest, but not Dictyostelium, and wildebeest having a much greater range of perception than cells, and thus being able to respond to environmental conditions tens of kilometres away. The toolbox of methods developed in this thesis could be applied to increase understanding of the mechanisms underlying collective movement in a wide range of systems. In their current form, these methods are capable of producing very close matches between models and data for our simple cell systems, and also produce a relatively good model fit in the more complex wildebeest system, where there is, however, still some room for improvement. While more work is required to make the models generalisable to all taxa, particularly through the addition of memory-driven movement, inter-individual differences in behaviour, and more complex social dynamics, the advection-diffusion modelling framework is flexible enough for these additional behaviours to be incorporated in the future. A greater understanding of what drives collective movements in different systems could allow management of these movements to prevent the collapse of important migrations, control pest species, or prevent the spread of cancer

Dynamical mean field modelling and estimation of neuronal oscillations

Oscillations in neural activity are a ubiquitous phenomenon in the brain. They span multiple timescales and correlate with a myriad of physiological and pathological conditions. Given their intrinsic dynamical nature, mathematical and computational modelling tools have proven to be indispensible in order to interpret and formalize the mechanisms through which these oscillations arise. In this Thesis, I developed a new methodological framework that allows the assimilation of experimental data into biophysically plausible models of neural oscillations. Motivated by the fast oscillatory activity (30 ~ 130 Hz) at the onset of focal epileptic seizures, I started by investigating, via means of bifurcation analyses, whether such fast oscillations can be plausibly described by conductance-based neural mass models. Neural mass models have enjoyed success in describing several forms of epileptiform activity (e.g. spike-and-wave seizures and interictal spikes), but I found that, in order to generate such fast oscillations, the parameters of this family of models would have to depart significantly from biophysical plausibility. These results motivated the exploration of full mean-field models of spiking neurons to characterise this type of dynamics. I hence proposed a variant of a mean-field neural population model based on the Fokker-Planck equation of conductance-based, stochastic, leaky integrate-and-fire neurons. This modelling approach was chosen for its capacity to describe arbitrary network configurations and predict firing rates, trans-membrane currents and local field potentials. I introduced a new numerical scheme that makes the computational cost of integrating the ensuing partial differential equations scale linearly with the number of nodes of the networks. These advances are crucial for the practical implementation of model inversion schemes. I then built upon the literature of Dynamic Causal Modelling to develop a Bayesian model inversion algorithm applicable to dynamical systems in limit cycle regimes. I applied the scheme to the mean-field models described above, using experimental data recordings of carbachol-induced gamma oscillations, in the CA1 region of mice hippocampal slice preparations. The estimated model was able to make accurate predictions about independent data features; namely inter-spike-interval distributions. Also, the inverted models were qualitatively compatible with the observation that excitatory pyramidal cells and inhibitory interneurons play equally important roles in the dynamics of these oscillations (as opposed to interneuron-dominated gamma oscillations). I also explored the applicability of this inversion scheme to neural mass models of electroencephalographically recorded spike-and-wave seizures in humans. In conclusion, the work presented in this thesis provides significant new contributions to model based analyses of neuronal oscillatory data, and helps to bridge single-neuron measurements to network-level interactions

Probabilistic Modeling of Rumour Stance and Popularity in Social Media

Social media tends to be rife with rumours when new reports are released piecemeal during breaking news events. One can mine multiple reactions expressed by social media users in those situations, exploring users’ stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. Moreover, rumours in social media exhibit complex temporal patterns. Some rumours are discussed with an increasing number of tweets per unit of time whereas other rumours fail to gain ground. This thesis develops probabilistic models of rumours in social media driven by two applications: rumour stance classification and modeling temporal dynamics of rumours. Rumour stance classification is the task of classifying the stance expressed in an individual tweet towards a rumour. Modeling temporal dynamics of rumours is an application where rumour prevalence is modeled over time. Both applications provide insights into how a rumour attracts attention from the social media community. These can assist journalists with their work on rumour tracking and debunking, and can be used in downstream applications such as systems for rumour veracity classification. In this thesis, we develop models based on probabilistic approaches. We motivate Gaussian processes and point processes as appropriate tools and show how features not considered in previous work can be included. We show that for both applications, transfer learning approaches are successful, supporting the hypothesis that there is a common underlying signal across different rumours. We furthermore introduce novel machine learning techniques which have the potential to be used in other applications: convolution kernels for streams of text over continuous time and a sequence classification algorithm based on point processes

Curve sampling and geometric conditional simulation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 195-203).The main objective of this thesis is the development and exploitation of techniques to generate geometric samples for the purpose of image segmentation. A sampling-based approach provides a number of benefits over existing optimization-based methods such as robustness to noise and model error, characterization of segmentation uncertainty, natural handling of multi-modal distributions, and incorporation of partial segmentation information. This is important for applications which suffer from, e.g., low signal-to-noise ratio (SNR) or ill-posedness. We create a curve sampling algorithm using the Metropolis-Hastings Markov chain Monte Carlo (MCMC) framework. With this method, samples from a target distribution [pi] (which can be evaluated but not sampled from directly) are generated by creating a Markov chain whose stationary distribution is [pi] and sampling many times from a proposal distribution q. We define a proposal distribution using random Gaussian curve perturbations, and show how to ensure detailed balance and ergodicity of the chain so that iterates of the Markov chain asymptotically converge to samples from [pi]. We visualize the resulting samples using techniques such as confidence bounds and principal modes of variation and demonstrate the algorithm on examples such as prostate magnetic resonance (MR) images, brain MR images, and geological structure estimation using surface gravity observations. We generalize our basic curve sampling framework to perform conditional simulation: a portion of the solution space is specified, and the remainder is sampled conditioned on that information. For difficult segmentation problems which are currently done manually by human experts, reliable semi-automatic segmentation approaches can significantly reduce the amount of time and effort expended on a problem. We also extend our framework to 3D by creating a hybrid 2D/3D Markov chain surface model.For this approach, the nodes on the chain represent entire curves on parallel planes,and the slices combine to form a complete surface. Interaction among the curves is described by an undirected Markov chain, and we describe methods to sample from this model using both local Metropolis-Hastings methods and the embedded hidden Markov model (HMM) algorithm.by Ayres C. Fan.Ph.D