A simple model for detection of rare sound events

We propose a simple recurrent model for detecting rare sound events, when the time boundaries of events are available for training. Our model optimizes the combination of an utterance-level loss, which classifies whether an event occurs in an utterance, and a frame-level loss, which classifies whether each frame corresponds to the event when it does occur. The two losses make use of a shared vectorial representation the event, and are connected by an attention mechanism. We demonstrate our model on Task 2 of the DCASE 2017 challenge, and achieve competitive performance.Comment: Accepted by Interspeech 201

Pathway and biomarker discovery in a posttraumatic stress disorder mouse model

Posttraumatic stress disorder (PTSD), a prevalent psychiatric disorder, is caused by exposure to a traumatic event. Individuals diagnosed for PTSD not only experience significant functional impairments but also have higher rates of physical morbidity and mortality. Despite intense research efforts, the neurobiological pathways affecting fear circuit brain regions in PTSD remain obscure and most of the previous studies were limited to characterization of specific markers in periphery or defined brain regions. In my PhD study, I employed proteomics, metabolomics and transcriptomcis technologies interrogating a foot shock induced PTSD mouse model. In addition, I studied the effects of early intervention of chronic fluoxetine treatment. By in silico analyses, altered cellular pathways associated with PTSD were identified in stress-vulnerable brain regions, including prelimbic cortex (PrL), anterior cingulate cortex (ACC), basolateral amygdala (BLA), central nucleus of amygdala(CeA), nucleus accumbens (NAc) and CA1 of the dorsal hippocampus. With RNA sequencing, I compared the brain transcriptome between shocked and control mice, with and without fluoxetine treatment. Differentially expressed genes were identified and clustered, and I observed increased inflammation in ACC and decreased neurotransmitter signaling in both ACC and CA1. I applied in vivo 15N metabolic labeling combined with mass spectrometry to study alterations at proteome level in the brain. By integrating proteomics and metabolomics profiling analyses, I found decreased Citric Acid Cycle pathway in both NAc and ACC, and dysregulated cytoskeleton assembly and myelination pathways in BLA, CeA and CA1. In addition, chronic fluoxetine treatment 12 hours after foot shock prevented altered inflammatory gene expression in ACC, and Citric Acid Cycle in NAc and ACC, and ameliorated conditioned fear response in shocked mice. These results shed light on the role of immune response and energy metabolism in PTSD pathogenesis. Furthermore, I performed microdialysis in medial prefrontal cortex and hippocampus to measure the changes in extracellular norepinephrine and free corticosterone (CORT) in the shocked mouse and related them to PTSD-like symptoms, including hyperaroual and contextual fear response. I found that increased free CORT was related to immediate stress response, whereas norepinephrine level, in a brain region specific manner, predicted arousal and contextual fear response one month after trauma. I also applied metabolomics analysis to investigate molecular changes in prefrontal microdialysates of shocked mice. Citric Acid Cycle, Glyoxylate and Dicarboxylate metabolism and Alanine, Aspartate and Glutamate metabolism pathways were found to be involved in foot shock induced hyperarousal. Taken together, my study provides novel insights into PTSD pathogenesis and suggests potential therapeutic applications targeting dysregulated pathways

A study on n-gram indexing of musical features

Since only simple symbol-based manipulations are needed, n-gram indexing is used for natural languages where syntactic or semantic analyses are often difficult. Music, whose automatic analysis for patterns such as motifs and phrases are difficult, inaccurate or computationally expensive, is thus similar to natural languages. The use of n-gram in music retrieval systems is thus a natural choice. In this paper, we study a number of issues regarding n-gram indexing of musical features using simulated queries. They are: whether combinatorial explosion is a problem in n-gram indexing of musical features, the relative discrimination power of six different musical features, the value of n needed for them, and the average amount of false positives returned when n-grams are used to index music.published_or_final_versio

Indexing multilingual information on the web

The web connects people speaking more than twenty languages in more than one hundred countries. Search engines, which provide users starting points to navigate and retrieve resources on the web, should thus be able to handle documents in many languages. Moreover, with information being added and changed every minute on the web, search engines should discover new index terms time-efficiently. This paper introduces an abstraction of viewing multilingual documents and a statistical analysis method so that search engines can index multilingual documents in a generic, efficient, and effective manner with minimal requirements of language-specific information.published_or_final_versio

Selection of melody lines for music databases

One major approach to music retrieval is to model music as a sequence of features, after which traditional information retrieval techniques are applied on the sequence. Because of the temporal nature of music and the inexactness of user queries, most effort on music retrieval systems focus on issues such as indexing and approximation match. In contrast, the processing of music before feature extraction, such as the identification of melody track, were often considered easy or done. This may be the case in a controlled environment, such as one for musicology research, where the pieces are carefully analyzed by human beings before being submitted to the database. However, in an environment where large volumes of music is obtained from the Web, manual music analysis is impractical. Since many well-known musical features often pertain to the melody of musical pieces, and users often remember the melody of a song, algorithms that select the melody tracks of a piece are important for Web-based content-based retrieval systems. In this paper, we describe a number of algorithms for automatic melody track selection in a music retrieval context. We will also study the performance of the algorithms by comparing their answers to those judged by human beings.published_or_final_versio

USING A LEAST SQUARES SUPPORT VECTOR MACHINE TO ESTIMATE A LOCAL GEOMETRIC GEOID MODEL

In this study, test-region global positioning system (GPS) control points exhibitingknown first-order orthometric heights were employed to obtain the points of planecoordinates and ellipsoidal heights by using the real-time GPS kinematicmeasurement method. Plane-fitting, second-order curve-surface fitting, back-propagation (BP) neural networks, and least-squares support vector machine (LS-SVM) calculation methods were employed. The study includes a discussion on dataintegrity and localization, changing reference-point quantities and distributions toobtain an optimal solution. Furthermore, the LS-SVM was combined with localgeoidal-undulation models that were established by researching and analyzing3kernel functions. The results indicated that the overall precision of the localgeometric geoidal-undulation values calculated using the radial basis function(RBF) and third-order polynomial kernel function was optimal and the root meansquare error (RMSE) was approximately ± 1.5 cm. These findings demonstrated thatthe LS-SVM provides a rapid and practical method for determining orthometricheights and should serve as a valuable academic reference regarding local geoidmodels

Pathway and biomarker discovery in a posttraumatic stress disorder mouse model

Posttraumatic stress disorder (PTSD), a prevalent psychiatric disorder, is caused by exposure to a traumatic event. Individuals diagnosed for PTSD not only experience significant functional impairments but also have higher rates of physical morbidity and mortality. Despite intense research efforts, the neurobiological pathways affecting fear circuit brain regions in PTSD remain obscure and most of the previous studies were limited to characterization of specific markers in periphery or defined brain regions. In my PhD study, I employed proteomics, metabolomics and transcriptomcis technologies interrogating a foot shock induced PTSD mouse model. In addition, I studied the effects of early intervention of chronic fluoxetine treatment. By in silico analyses, altered cellular pathways associated with PTSD were identified in stress-vulnerable brain regions, including prelimbic cortex (PrL), anterior cingulate cortex (ACC), basolateral amygdala (BLA), central nucleus of amygdala(CeA), nucleus accumbens (NAc) and CA1 of the dorsal hippocampus. With RNA sequencing, I compared the brain transcriptome between shocked and control mice, with and without fluoxetine treatment. Differentially expressed genes were identified and clustered, and I observed increased inflammation in ACC and decreased neurotransmitter signaling in both ACC and CA1. I applied in vivo 15N metabolic labeling combined with mass spectrometry to study alterations at proteome level in the brain. By integrating proteomics and metabolomics profiling analyses, I found decreased Citric Acid Cycle pathway in both NAc and ACC, and dysregulated cytoskeleton assembly and myelination pathways in BLA, CeA and CA1. In addition, chronic fluoxetine treatment 12 hours after foot shock prevented altered inflammatory gene expression in ACC, and Citric Acid Cycle in NAc and ACC, and ameliorated conditioned fear response in shocked mice. These results shed light on the role of immune response and energy metabolism in PTSD pathogenesis. Furthermore, I performed microdialysis in medial prefrontal cortex and hippocampus to measure the changes in extracellular norepinephrine and free corticosterone (CORT) in the shocked mouse and related them to PTSD-like symptoms, including hyperaroual and contextual fear response. I found that increased free CORT was related to immediate stress response, whereas norepinephrine level, in a brain region specific manner, predicted arousal and contextual fear response one month after trauma. I also applied metabolomics analysis to investigate molecular changes in prefrontal microdialysates of shocked mice. Citric Acid Cycle, Glyoxylate and Dicarboxylate metabolism and Alanine, Aspartate and Glutamate metabolism pathways were found to be involved in foot shock induced hyperarousal. Taken together, my study provides novel insights into PTSD pathogenesis and suggests potential therapeutic applications targeting dysregulated pathways