A Review of Subsequence Time Series Clustering

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies

The EM Algorithm and the Rise of Computational Biology

In the past decade computational biology has grown from a cottage industry with a handful of researchers to an attractive interdisciplinary field, catching the attention and imagination of many quantitatively-minded scientists. Of interest to us is the key role played by the EM algorithm during this transformation. We survey the use of the EM algorithm in a few important computational biology problems surrounding the "central dogma"; of molecular biology: from DNA to RNA and then to proteins. Topics of this article include sequence motif discovery, protein sequence alignment, population genetics, evolutionary models and mRNA expression microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Generation of Two-Voice Imitative Counterpoint from Statistical Models

Generating new music based on rules of counterpoint has been deeply studied in music informatics. In this article, we try to go further, exploring a method for generating new music based on the style of Palestrina, based on combining statistical generation and pattern discovery. A template piece is used for pattern discovery, and the patterns are selected and organized according to a probabilistic distribution, using horizontal viewpoints to describe melodic properties of events. Once the template is covered with patterns, two-voice counterpoint in a florid style is generated into those patterns using a first-order Markov model. The template method solves the problem of coherence and imitation never addressed before in previous research in counterpoint music generation. For constructing the Markov model, vertical slices of pitch and rhythm are compiled over a large corpus of dyads from Palestrina masses. The template enforces different restrictions that filter the possible paths through the generation process. A double backtracking algorithm is implemented to handle cases where no solutions are found at some point within a generation path. Results are evaluated by both information content and listener evaluation, and the paper concludes with a proposed relationship between musical quality and information content. Part of this research has been presented at SMC 2016 in Hamburg, Germany

Applications of high-frequency telematics for driving behavior analysis

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and EconometricsProcessing driving data and investigating driving behavior has been receiving an increasing interest in the last decades, with applications ranging from car insurance pricing to policy-making. A popular way of analyzing driving behavior is to move the focus to the maneuvers as they give useful information about the driver who is performing them. Previous research on maneuver detection can be divided into two strategies, namely, 1) using fixed thresholds in inertial measurements to define the start and end of specific maneuvers or 2) using features extracted from rolling windows of sensor data in a supervised learning model to detect maneuvers. While the first strategy is not adaptable and requires fine-tuning, the second needs a dataset with labels (which is time-consuming) and cannot identify maneuvers with different lengths in time. To tackle these shortcomings, we investigate a new way of identifying maneuvers from vehicle telematics data, through motif detection in time-series. Using a publicly available naturalistic driving dataset (the UAH-DriveSet), we conclude that motif detection algorithms are not only capable of extracting simple maneuvers such as accelerations, brakes, and turns, but also more complex maneuvers, such as lane changes and overtaking maneuvers, thus validating motif discovery as a worthwhile line for future research in driving behavior. We also propose TripMD, a system that extracts the most relevant driving patterns from sensor recordings (such as acceleration) and provides a visualization that allows for an easy investigation. We test TripMD in the same UAH-DriveSet dataset and show that (1) our system can extract a rich number of driving patterns from a single driver that are meaningful to understand driving behaviors and (2) our system can be used to identify the driving behavior of an unknown driver from a set of drivers whose behavior we know.Nas últimas décadas, o processamento e análise de dados de condução tem recebido um interesse cada vez maior, com aplicações que abrangem a área de seguros de automóveis até a atea de regulação. Tipicamente, a análise de condução compreende a extração e estudo de manobras uma vez que estas contêm informação relevante sobre a performance do condutor. A investigação prévia sobre este tema pode ser dividida em dois tipos de estratégias, a saber, 1) o uso de valores fixos de aceleração para definir o início e fim de cada manobra ou 2) a utilização de modelos de aprendizagem supervisionada em janelas temporais. Enquanto o primeiro tipo de estratégias é inflexível e requer afinação dos parâmetros, o segundo precisa de dados de condução anotados (o que é moroso) e não é capaz de identificar manobras de diferentes durações. De forma a mitigar estas lacunas, neste trabalho, aplicamos métodos desenvolvidos na área de investigação de séries temporais de forma a resolver o problema de deteção de manobras. Em particular, exploramos área de deteção de motifs em séries temporais e testamos se estes métodos genéricos são bem-sucedidos na deteção de manobras. Também propomos o TripMD, um sistema que extrai os padrões de condução mais relevantes de um conjuntos de viagens e fornece uma simples visualização. TripMD é testado num conjunto de dados públicos (o UAH-DriveSet), do qual concluímos que (1) o nosso sistema é capaz de extrair padrões de condução/manobras de um único condutor que estão relacionados com o perfil de condução do condutor em questão e (2) o nosso sistema pode ser usado para identificar o perfil de condução de um condutor desconhecido de um conjunto de condutores cujo comportamento nos é conhecido

Mapping the proteome with data-driven methods: A cycle of measurement, modeling, hypothesis generation, and engineering

The living cell exhibits emergence of complex behavior and its modeling requires a systemic, integrative approach if we are to thoroughly understand and harness it. The work in this thesis has had the more narrow aim of quantitatively characterizing and mapping the proteome using data-driven methods, as proteins perform most functional and structural roles within the cell. Covered are the different parts of the cycle from improving quantification methods, to deriving protein features relying on their primary structure, predicting the protein content solely from sequence data, and, finally, to developing theoretical protein engineering tools, leading back to experiment.\ua0\ua0\ua0\ua0 High-throughput mass spectrometry platforms provide detailed snapshots of a cell\u27s protein content, which can be mined towards understanding how the phenotype arises from genotype and the interplay between the various properties of the constituent proteins. However, these large and dense data present an increased analysis challenge and current methods capture only a small fraction of signal. The first part of my work has involved tackling these issues with the implementation of a GPU-accelerated and distributed signal decomposition pipeline, making factorization of large proteomics scans feasible and efficient. The pipeline yields individual analyte signals spanning the majority of acquired signal, enabling high precision quantification and further analytical tasks.\ua0\ua0\ua0 Having such detailed snapshots of the proteome enables a multitude of undertakings. One application has been to use a deep neural network model to learn the amino acid sequence determinants of temperature adaptation, in the form of reusable deep model features. More generally, systemic quantities may be predicted from the information encoded in sequence by evolutionary pressure. Two studies taking inspiration from natural language processing have sought to learn the grammars behind the languages of expression, in one case predicting mRNA levels from DNA sequence, and in the other protein abundance from amino acid sequence. These two models helped build a quantitative understanding of the central dogma and, furthermore, in combination yielded an improved predictor of protein amount. Finally, a mathematical framework relying on the embedded space of a deep model has been constructed to assist guided mutation of proteins towards optimizing their abundance