Mauricio A. Álvarez

ANÁLISIS DE PROXIMIDAD DE MODELOS OCULTOS DE MARKOV PARA LA IDENTIFICACIÓN DE FUENTES DE ESPIGAS

Recently, Hidden Markov Models (HMM) have been used for the identification of spike sources in Parkinson’s disease treatment. Usual classification criteria consists in maximum a posteriori rule (MAP) for the recognition of the correct class. However, classification could be improved using proximity analysis, transforming matrices of Markov process to another space where similarities and differences are better revealed. The authors present the proximity analysis approach using HMM for the identification of spike sources (Thalamo and Subthalamo, Gpi and GPe). Results show that proximity analysis improves recognition performance for about 4% over traditional approach.

Shape Classification Using Hilbert Space Embeddings and Kernel Adaptive Filtering

Shape classification is employed for realizing image object identification and classification tasks. Most of the state-of-the-art approaches use sequential features extracted from contours to classify shapes, either directly, i.e., k-nearest neighbors (KNN), or through stochastic models, i.e., hidden Markov models (HMMs). Here, inspired by probability based metrics using Hilbert space embedding (HSE), we introduce a novel scheme for efficient shape classification. To this end, we highlight relevant curvature patterns from binary images towards a Kernel Adaptive Filtering (KAF)-based enhancement of the maximum mean discrepancy metric. Namely, we test the performance of our approach on the well-known MPEG-7 and 99-Shapes databases. Results show that our strategy can code relevant shape properties from binary images achieving competitive classification results.

Impulse Response Estimation of Linear Time-Invariant Systems Using Convolved Gaussian Processes and Laguerre Functions

This paper presents a novel method to estimate the impulse response function of Linear Time-Invariant systems from input-output data by means of Laguerre functions and Convolved Gaussian Processes. We define a new non-stationary covariance function that encodes the convolution between the Laguerre functions and the input. The input (excitation) is modelled by a Gaussian Process prior. Thus, we are able to estimate the system’s impulse response by performing maximum likelihood estimation over the model hyperparameters. Besides, the proposed model performs well in missing and noisy data scenarios.

Non-stationary Multi-output Gaussian Processes for Enhancing Resolution over Diffusion Tensor Fields

Diffusion magnetic resonance imaging (dMRI) is an advanced technique derived from magnetic resonance imaging (MRI) that allows the study of internal structures in biological tissue. Due to acquisition protocols and hardware limitations of the equipment employed to obtain the data, the spatial resolution of the images is often low. This inherent lack in dMRI is a considerable difficulty because clinical applications are affected. The scientific community has proposed several methodologies for enhancing the spatial resolution of dMRI data, based on interpolation of diffusion tensors fields. However, most of the methods have considerable drawbacks when they interpolate strong transitions, such as crossing fibers. Also, relevant clinical information from tensor fields is modified when interpolation is performed. In this work, we propose a probabilistic methodology for interpolation of diffusion tensors fields using multi-output Gaussian processes with non-stationary kernel function. First, each tensor is decomposed in shape and orientation features. Then, the model interpolates the features jointly. Results show that proposed approach outperforms state-of-the-art methods regarding resolution enhancement accuracy on synthetic and real data, when we evaluate interpolation quality with Frobenius and Riemann metrics. Also, the proposed method demonstrates an adequate characterization of both stationary and non-stationary fields, contrary to previous approaches where performance is seriously reduced when complex fields are interpolated.

Short-term time series prediction using Hilbert space embeddings of autoregressive processes

Linear autoregressive models serve as basic representations of discrete time stochastic processes. Different attempts have been made to provide non-linear versions of the basic autoregressive process, including different versions based on kernel methods. Motivated by the powerful framework of Hilbert space embeddings of distributions, in this paper we apply this methodology for the kernel embedding of an autoregressive process of order

Dynamic facial landmarking selection for emotion recognition using Gaussian processes

p

Switched latent force models for reverse-engineering transcriptional regulation in gene expression data

. By doing so, we provide a non-linear version of an autoregressive process, that shows increased performance over the linear model in highly complex time series. We use the method proposed for one-step ahead forecasting of different time-series, and compare its performance against other non-linear methods.

Approximate Probabilistic Power Flow

Facial features are the basis for the emotion recognition process and are widely used in affective computing systems. This emotional process is produced by a dynamic change in the physiological signals and the visual answers related to the facial expressions. An important factor in this process, relies on the shape information of a facial expression, represented as dynamically changing facial landmarks. In this paper we present a framework for dynamic facial landmarking selection based on facial expression analysis using Gaussian Processes. We perform facial features tracking, based on Active Appearance Models for facial landmarking detection, and then use Gaussian process ranking over the dynamic emotional sequences with the aim to establish which landmarks are more relevant for emotional multivariate time-series recognition. The experimental results show that Gaussian Processes can effectively fit to an emotional time-series and the ranking process with log-likelihoods finds the best landmarks (mouth and eyebrows regions) that represent a given facial expression sequence. Finally, we use the best ranked landmarks in emotion recognition tasks obtaining accurate performances for acted and spontaneous scenarios of emotional datasets.

Optimal state selection and tuning parameters for a degradation model in bearings using Mel-Frequency Cepstral Coefficients and Hidden Markov Chains

To survive environmental conditions, cells transcribe their response activities into encoded mRNA sequences in order to produce certain amounts of protein concentrations. The external conditions are mapped into the cell through the activation of special proteins called transcription factors (TFs). Due to the difficult task to measure experimentally TF behaviors, and the challenges to capture their quick-time dynamics, different types of models based on differential equations have been proposed. However, those approaches usually incur in costly procedures, and they present problems to describe sudden changes in TF regulators. In this paper, we present a switched dynamical latent force model for reverse-engineering transcriptional regulation in gene expression data which allows the exact inference over latent TF activities driving some observed gene expressions through a linear differential equation. To deal with discontinuities in the dynamics, we introduce an approach that switches between different TF activities and different dynamical systems. This creates a versatile representation of transcription networks that can capture discrete changes and non-linearities. We evaluate our model on both simulated data and real data (e.g., microaerobic shift in E. coli, yeast respiration), concluding that our framework allows for the fitting of the expression data while being able to infer continuous-time TF profiles.

Control adaptivo por reubicación de polos

Power flow analysis is a necessary tool for operating and planning Power systems. This tool uses a deterministic approach for obtaining the steady state of the system for a specified set of power generation, loads, and network conditions. However this deterministic methodology does not take into account the uncertainty in the power systems, for example the variability in power generation, variation in the demand, changes in network configuration. The probabilistic power flow (PPF) study has been used as an useful tool to consider the system uncertainties in power systems. In this paper, we propose another alternative for solving the PPF problem. This paper shows a formulation of the PPF problem under a Bayesian inference perspective and also presents an approximate Bayesian inference method as a suitable solution of a PPF problem. The proposed method assumes a solution drew from a prior distribution, then it obtains simulated data (active and reactive power injected) using power flow equations and finally compares the observed data and simulated data for accepting the solution or rejecting these variables. This overall procedure is known as Approximate Bayesian Computation (ABC). An experimental comparison between the proposed methodology and traditional Monte Carlo simulation is also shown. The proposed methods have been applied on a 6 bus test system and 39 bus test system modified to include a wind farm. Results show that the proposed methodology based on ABC is another alternative for solving the probabilistic power flow problem; similarly this approximate method take less computation time for obtaining the probabilistic solution with respect to the state-of-the-art methodologies.