Learning sound representations using trainable COPE feature extractors

Sound analysis research has mainly been focused on speech and music processing. The deployed methodologies are not suitable for analysis of sounds with varying background noise, in many cases with very low signal-to-noise ratio (SNR). In this paper, we present a method for the detection of patterns of interest in audio signals. We propose novel trainable feature extractors, which we call COPE (Combination of Peaks of Energy). The structure of a COPE feature extractor is determined using a single prototype sound pattern in an automatic configuration process, which is a type of representation learning. We construct a set of COPE feature extractors, configured on a number of training patterns. Then we take their responses to build feature vectors that we use in combination with a classifier to detect and classify patterns of interest in audio signals. We carried out experiments on four public data sets: MIVIA audio events, MIVIA road events, ESC-10 and TU Dortmund data sets. The results that we achieved (recognition rate equal to 91.71% on the MIVIA audio events, 94% on the MIVIA road events, 81.25% on the ESC-10 and 94.27% on the TU Dortmund) demonstrate the effectiveness of the proposed method and are higher than the ones obtained by other existing approaches. The COPE feature extractors have high robustness to variations of SNR. Real-time performance is achieved even when the value of a large number of features is computed.Comment: Accepted for publication in Pattern Recognitio

Trisomy 21 alters DNA methylation in parent-of-origin-dependent and independent manners

The supernumerary chromosome 21 in Down syndrome differentially affects the methylation statuses at CpG dinucleotide sites and creates genome-wide transcriptional dysregulation of parental alleles, ultimately causing diverse pathologies. At present, it is unknown whether those effects are dependent or independent of the parental origin of the nondis-joined chromosome 21. Linkage analysis is a standard method for the determination of the parental origin of this aneuploidy, although it is inadequate in cases with deficiency of samples from the progenitors. Here, we assessed the reliability of the epigenetic 5(m)CpG imprints resulting in the maternally (oocyte)-derived allele methylation at a differentially methylated region (DMR) of the candidate imprinted WRB gene for asserting the parental origin of chromosome 21. We developed a methylation-sensitive restriction enzyme-specific PCR assay, based on the WRB DMR, across single nucleotide polymorphisms (SNPs) to examine the methylation statuses in the parental alleles. In genomic DNA from blood cells of either disomic or trisomic subjects, the maternal alleles were consistently methylated, while the paternal alleles were unmethylated. However, the supernumerary chromosome 21 did alter the methylation patterns at the RUNX1 (chromosome 21) and TMEM131 (chromosome 2) CpG sites in a parent-of-origin-independent manner. To evaluate the 5(m)CpG imprints, we conducted a computational comparative epigenomic analysis of transcriptome RNA sequencing (RNA-Seq) and histone modification expression patterns. We found allele fractions consistent with the transcriptional biallelic expression of WRB and ten neighboring genes, despite the similarities in the confluence of both a 17-histone modification activation backbone module and a 5-histone modification repressive module between the WRB DMR and the DMRs of six imprinted genes. We concluded that the maternally inherited 5(m)CpG imprints at the WRB DMR are uncoupled from the parental allele expression of WRB and ten neighboring genes in several tissues and that trisomy 21 alters DNA methylation in parent-of-origin-dependent and -independent manners

Breaking Computational Barriers to Perform Time Series Pattern Mining at Scale and at the Edge

Uncovering repeated behavior in time series is an important problem in many domains such as medicine, geophysics, meteorology, and many more. With the continuing surge of smart/embedded devices generating time series data, there is an ever growing need to perform analysis on datasets of increasing size. Additionally, there is an increasing need for analysis at low power edge devices due to latency problems inherent to the speed of light and the sheer amount of data being recorded. The matrix profile has proven to be a tool highly suitable for pattern mining in time series; however, a naive approach to computing the matrix profile makes it impossible to use effectively in both the cloud and at the edge. This dissertation shows how, through the use of GPUs and machine learning, the matrix profile is computed more feasibly, both at cloud-scale and at sensor-scale. In addition, it illustrates why both of these types of computation are important and what new insights they can provide to practitioners working with time series data

Conserved DNA sequence features underlie pervasive RNA polymerase pausing

Pausing of transcribing RNA polymerase is regulated and creates opportunities to control gene expression. Research in metazoans has so far mainly focused on RNA polymerase II (Pol II) promoter-proximal pausing leaving the pervasive nature of pausing and its regulatory potential in mammalian cells unclear. Here, we developed a pause detecting algorithm (PDA) for nucleotide-resolution occupancy data and a new native elongating transcript sequencing approach, termed nested NET-seq, that strongly reduces artifactual peaks commonly misinterpreted as pausing sites. Leveraging PDA and nested NET-seq reveal widespread genome-wide Pol II pausing at single-nucleotide resolution in human cells. Notably, the majority of Pol II pauses occur outside of promoter-proximal gene regions primarily along the gene-body of transcribed genes. Sequence analysis combined with machine learning modeling reveals DNA sequence properties underlying widespread transcriptional pausing including a new pause motif. Interestingly, key sequence determinants of RNA polymerase pausing are conserved between human cells and bacteria. These studies indicate pervasive sequence-induced transcriptional pausing in human cells and the knowledge of exact pause locations implies potential functional roles in gene expression

Machine Learning for Physiological Time Series: Representing and Controlling Blood Glucose for Diabetes Management

Type 1 diabetes is a chronic health condition affecting over one million patients in the US, where blood glucose (sugar) levels are not well regulated by the body. Researchers have sought to use physiological data (e.g., blood glucose measurements) collected from wearable devices to manage this disease, either by forecasting future blood glucose levels for predictive alarms, or by automating insulin delivery for blood glucose management. However, the application of machine learning (ML) to these data is hampered by latent context, limited supervision and complex temporal dependencies. To address these challenges, we develop and evaluate novel ML approaches in the context of i) representing physiological time series, particularly for forecasting blood glucose values and ii) decision making for when and how much insulin to deliver. When learning representations, we leverage the structure of the physiological sequence as an implicit information stream. In particular, we a) incorporate latent context when predicting adverse events by jointly modeling patterns in the data and the context those patterns occurred under, b) propose novel types of self-supervision to handle limited data and c) propose deep models that predict functions underlying trajectories to encode temporal dependencies. In the context of decision making, we use reinforcement learning (RL) for blood glucose management. Through the use of an FDA-approved simulator of the glucoregulatory system, we achieve strong performance using deep RL with and without human intervention. However, the success of RL typically depends on realistic simulators or experimental real-world deployment, neither of which are currently practical for problems in health. Thus, we propose techniques for leveraging imperfect simulators and observational data. Beyond diabetes, representing and managing physiological signals is an important problem. By adapting techniques to better leverage the structure inherent in the data we can help overcome these challenges.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163134/1/ifox_1.pd

The things (we think) we (ought to) do: Ideological processes and practices in teaching

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