Definition and composition of motor primitives using latent force models and hidden Markov models

In this work a different probabilistic motor primitive parameterization is proposed using latent force models (LFMs). The sequential composition of different motor primitives is also addressed using hidden Markov models (HMMs) which allows to capture the redundancy over dynamics by using a limited set of hidden primitives. The capability of the proposed model to learn and identify motor primitive occurrences over unseen movement realizations is validated using synthetic and motion capture data

Single-molecule studies of conformational states and dynamics in the ABC importer OpuA

The current model of active transport via ABC importers is mostly based on structural, biochemical and genetic data. We here establish single-molecule Förster resonance energy transfer (smFRET) assays to monitor the conformational states and heterogeneity of the osmoregulatory type I ABC importer OpuA from Lactococcus~lactis. We present data probing both intradomain distances that elucidate conformational changes within the substrate-binding domain (SBD) OpuAC, and interdomain distances between SBDs or transmembrane domains. Using this methodology, we studied ligand-binding mechanisms, as well as ATP and glycine betaine dependences of conformational changes. Our work expands the scope of smFRET investigations towards a class of so far unstudied ABC importers, and paves the way for a full understanding of their transport cycle in the future

Marimba:A tool for verifying properties of hidden markov models

The formal verification of properties of Hidden Markov Models (HMMs) is highly desirable for gaining confidence in the correctness of the model and the corresponding system. A significant step towards HMM verification was the development by Zhang et al. of a family of logics for verifying HMMs, called POCTL*, and its model checking algorithm. As far as we know, the verification tool we present here is the first one based on Zhang et al.'s approach. As an example of its effective application, we verify properties of a handover task in the context of human-robot interaction. Our tool was implemented in Haskell, and the experimental evaluation was performed using the humanoid robot Bert2.Comment: Tool paper accepted in the 13th International Symposium on Automated Technology for Verification and Analysis (ATVA 2015

Generalized iterated wreath products of cyclic groups and rooted trees correspondence

Consider the generalized iterated wreath product $\mathbb{Z}_{r_1}\wr \mathbb{Z}_{r_2}\wr \ldots \wr \mathbb{Z}_{r_k}$ where $r_i \in \mathbb{N}$. We prove that the irreducible representations for this class of groups are indexed by a certain type of rooted trees. This provides a Bratteli diagram for the generalized iterated wreath product, a simple recursion formula for the number of irreducible representations, and a strategy to calculate the dimension of each irreducible representation. We calculate explicitly fast Fourier transforms (FFT) for this class of groups, giving literature's fastest FFT upper bound estimate.Comment: 15 pages, to appear in Advances in the Mathematical Science

FIBS: A Generic Framework for Classifying Interval-based Temporal Sequences

We study the problem of classifying interval-based temporal sequences (IBTSs). Since common classification algorithms cannot be directly applied to IBTSs, the main challenge is to define a set of features that effectively represents the data such that classifiers can be applied. Most prior work utilizes frequent pattern mining to define a feature set based on discovered patterns. However, frequent pattern mining is computationally expensive and often discovers many irrelevant patterns. To address this shortcoming, we propose the FIBS framework for classifying IBTSs. FIBS extracts features relevant to classification from IBTSs based on relative frequency and temporal relations. To avoid selecting irrelevant features, a filter-based selection strategy is incorporated into FIBS. Our empirical evaluation on eight real-world datasets demonstrates the effectiveness of our methods in practice. The results provide evidence that FIBS effectively represents IBTSs for classification algorithms, which contributes to similar or significantly better accuracy compared to state-of-the-art competitors. It also suggests that the feature selection strategy is beneficial to FIBS's performance.Comment: In: Big Data Analytics and Knowledge Discovery. DaWaK 2020. Springer, Cha

Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals

Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN

Gaussian Process Pseudo-Likelihood Models for Sequence Labeling

Several machine learning problems arising in natural language processing can be modeled as a sequence labeling problem. We provide Gaussian process models based on pseudo-likelihood approximation to perform sequence labeling. Gaussian processes (GPs) provide a Bayesian approach to learning in a kernel based framework. The pseudo-likelihood model enables one to capture long range dependencies among the output components of the sequence without becoming computationally intractable. We use an efficient variational Gaussian approximation method to perform inference in the proposed model. We also provide an iterative algorithm which can effectively make use of the information from the neighboring labels to perform prediction. The ability to capture long range dependencies makes the proposed approach useful for a wide range of sequence labeling problems. Numerical experiments on some sequence labeling data sets demonstrate the usefulness of the proposed approach.Comment: 18 pages, 5 figure

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

The adoption of high-accuracy speech recognition algorithms without an effective evaluation of their impact on the target computational resource is impractical for mobile and embedded systems. In this paper, techniques are adopted to minimise the required computational resource for an effective mobile-based speech recognition system. A Dynamic Multi-Layer Perceptron speech recognition technique, capable of running in real time on a state-of-the-art mobile device, has been introduced. Even though a conventional hidden Markov model when applied to the same dataset slightly outperformed our approach, its processing time is much higher. The Dynamic Multi-layer Perceptron presented here has an accuracy level of 96.94% and runs significantly faster than similar techniques

Haplotype inference based on Hidden Markov Models in the QTL-MAS 2010 multi-generational dataset

<p>Abstract</p> <p>Background</p> <p>We have previously demonstrated an approach for efficient computation of genotype probabilities, and more generally probabilities of allele inheritance in inbred as well as outbred populations. That work also included an extension for haplotype inference, or phasing, using Hidden Markov Models. Computational phasing of multi-thousand marker datasets has not become common as of yet. In this communication, we further investigate the method presented earlier for such problems, in a multi-generational dataset simulated for QTL detection.</p> <p>Results</p> <p>When analyzing the dataset simulated for the 14th QTLMAS workshop, the phasing produced showed zero deviations compared to original simulated phase in the founder generation. In total, 99.93% of all markers were correctly phased. 97.68% of the individuals were correct in all markers over all 5 simulated chromosomes. Results were produced over a weekend on a small computational cluster. The specific algorithmic adaptations needed for the Markov model training approach in order to reach convergence are described.</p> <p>Conclusions</p> <p>Our method provides efficient, near-perfect haplotype inference allowing the determination of completely phased genomes in dense pedigrees. These developments are of special value for applications where marker alleles are not corresponding directly to QTL alleles, thus necessitating tracking of allele origin, and in complex multi-generational crosses. The cnF2freq codebase, which is in a current state of active development, is available under a BSD-style license.</p