Explicit-duration Hidden Markov Model Research Articles

Lokatt: a hybrid DNA nanopore basecaller with an explicit duration hidden Markov model and a residual LSTM network

BackgroundBasecalling long DNA sequences is a crucial step in nanopore-based DNA sequencing protocols. In recent years, the CTC-RNN model has become the leading basecalling model, supplanting preceding hidden Markov models (HMMs) that relied on pre-segmenting ion current measurements. However, the CTC-RNN model operates independently of prior biological and physical insights.ResultsWe present a novel basecaller named Lokatt: explicit duration Markov model and residual-LSTM network. It leverages an explicit duration HMM (EDHMM) designed to model the nanopore sequencing processes. Trained on a newly generated library with methylation-free Ecoli samples and MinION R9.4.1 chemistry, the Lokatt basecaller achieves basecalling performances with a median single read identity score of 0.930, a genome coverage ratio of 99.750%, on par with existing state-of-the-art structure when trained on the same datasets.ConclusionOur research underlines the potential of incorporating prior knowledge into the basecalling processes, particularly through integrating HMMs and recurrent neural networks. The Lokatt basecaller showcases the efficacy of a hybrid approach, emphasizing its capacity to achieve high-quality basecalling performance while accommodating the nuances of nanopore sequencing. These outcomes pave the way for advanced basecalling methodologies, with potential implications for enhancing the accuracy and efficiency of nanopore-based DNA sequencing protocols.

Towards automated, integrated and unsupervised diagnosis of rolling element bearings

In the presence of faults, vibrations of rolling element bearings show symptomatic signatures in the form of repetitive impulses. This can be seen as a non-stationary signal whose statistical properties switch intermittently between two states. Based on this observation, the paper introduces an automated diagnosis framework that integrates the successive steps of fault detection, fault identification and fault characterization. The advantage is that the complete diagnosis process is completed at once, while involving a limited number of hyperparameters. The approach relies on modeling the raw vibration signal with an explicit-duration hidden Markov model (EDHMM) and then uses the estimated model parameters for diagnosis. The detection of a fault is first achieved by means of a likelihood ratio test built on the EDHMM parameters. Posterior probabilities are then used for identifying the fault type automatically. Finally, the fault size is estimated from the duration times returned by the EDHMM. The effectiveness of the proposed method is illustrated on independent experimental datasets.

Explicit-duration Hidden Markov Models for quantum state estimation

An explicit-duration Hidden Markov Model with a nonparametric kernel estimator of the state duration distribution is specified. The motivation comes from the physical problem of extracting the maximum information from an open quantum system subject to an external perturbation, which induces a change in the dynamics of the system. A nonparametric kernel estimator for discrete data is introduced, which is consistent and improves the estimates accuracy in presence of sparse data. To reconstruct the hidden dynamics, a Viterbi algorithm is used, which is robust against the underflow problem. Finite sample properties are investigated through an extensive Monte Carlo study showing that our formulation outperforms the original one both in small and in large samples.

Online Unsupervised Occupancy Anticipation System Applied to Residential Heat Load Management

Human preferences and lifestyles significantly impact buildings' energy consumption. Consequently, a better understanding of occupants' behavior is crucial to decrease energy consumption and maintain occupants' comfort. Occupant-centric control (OCC) strategies are effective approaches to fulfil such a purpose. As such, occupancy detection and prediction are of prime importance, particularly to manage Electric Space Heating (ESH) systems, due to the relatively slow dynamics of the temperature in dwellings. This paper proposes an Explicit Duration Hidden Markov Model (EDHMM) for unsupervised online presence detection and a hazard-based approach for occupancy prediction. Moreover, a control strategy using a cost function, weighted by occupancy predictions, and a load-shifting strategy based on time-varying electricity price are put forward. This work initially validates the consistency of the proposed approach by using synthetic data generated by a Monte Carlo simulation. Subsequently, the performance of our framework is compared with previous methods presented in the literature through experimental validation. Results demonstrate that the proposed EDHMM approach is efficient in detecting occupancy states. Besides, the results of the field implementation show the potential of the proposed control strategy to preserve occupants' thermal comfort while decreasing the heating energy consumption.

Machine Learning Based Network Analysis Using Millimeter-Wave Narrow-Band Energy Traces

Next-generation wireless networks promise to provide extremely high data rates, especially exploiting the so-called millimeter-wave frequency range. Gaining information from spectrum usage is becoming important to provide smart adaptation capabilities to future network protocol stacks. Issues such as deafness, misaligned antennas, or blockage may severely impact network performance, and their identification is crucial. Despite the complexity of full analytical models, machine learning techniques are progressively being considered to improve spectrum usage at higher layers. In this paper, we design a signal processing technique that uses narrowband physical layer energy traces, obtained from one or multiple channel sniffers. The proposed technique utilizes a combination of template matching and an Explicit Duration Hidden Markov Model (EDHMM) to correctly classify frames, while coping with the non-stationarity of the traces. This leads to a protocol level monitor that does not need to decode the channel at the physical layer, but just infers the type of packets that are exchanged based on sub-sampled energy traces. The performance of this framework is evaluated using off-the-shelf mm-wave wireless devices, quantifying its detection performance in the presence of one or multiple sniffers, and assessing the impact of physical layer parameters such as noise power and signal levels.

Explicit-duration hidden Markov model inference of UP-DOWN states from continuous signals.

Neocortical neurons show UP-DOWN state (UDS) oscillations under a variety of conditions. These UDS have been extensively studied because of the insight they can yield into the functioning of cortical networks, and their proposed role in putative memory formation. A key element in these studies is determining the precise duration and timing of the UDS. These states are typically determined from the membrane potential of one or a small number of cells, which is often not sufficient to reliably estimate the state of an ensemble of neocortical neurons. The local field potential (LFP) provides an attractive method for determining the state of a patch of cortex with high spatio-temporal resolution; however current methods for inferring UDS from LFP signals lack the robustness and flexibility to be applicable when UDS properties may vary substantially within and across experiments. Here we present an explicit-duration hidden Markov model (EDHMM) framework that is sufficiently general to allow statistically principled inference of UDS from different types of signals (membrane potential, LFP, EEG), combinations of signals (e.g., multichannel LFP recordings) and signal features over long recordings where substantial non-stationarities are present. Using cortical LFPs recorded from urethane-anesthetized mice, we demonstrate that the proposed method allows robust inference of UDS. To illustrate the flexibility of the algorithm we show that it performs well on EEG recordings as well. We then validate these results using simultaneous recordings of the LFP and membrane potential (MP) of nearby cortical neurons, showing that our method offers significant improvements over standard methods. These results could be useful for determining functional connectivity of different brain regions, as well as understanding network dynamics.

Prosody dependent speech recognition on radio news corpus of American English

Does prosody help word recognition? This paper proposes a novel probabilistic framework in which word and phoneme are dependent on prosody in a way that reduces word error rates (WER) relative to a prosody-independent recognizer with comparable parameter count. In the proposed prosody-dependent speech recognizer, word and phoneme models are conditioned on two important prosodic variables: the intonational phrase boundary and the pitch accent. An information-theoretic analysis is provided to show that prosody dependent acoustic and language modeling can increase the mutual information between the true word hypothesis and the acoustic observation by exciting the interaction between prosody dependent acoustic model and prosody dependent language model. Empirically, results indicate that the influence of these prosodic variables on allophonic models are mainly restricted to a small subset of distributions: the duration PDFs (modeled using an explicit duration hidden Markov model or EDHMM) and the acoustic-prosodic observation PDFs (normalized pitch frequency). Influence of prosody on cepstral features is limited to a subset of phonemes: for example, vowels may be influenced by both accent and phrase position, but phrase-initial and phrase-final consonants are independent of accent. Leveraging these results, effective prosody dependent allophonic models are built with minimal increase in parameter count. These prosody dependent speech recognizers are able to reduce word error rates by up to 11% relative to prosody independent recognizers with comparable parameter count, in experiments based on the prosodically-transcribed Boston Radio News corpus.