Advances in computer technology have enabled the collection, digitization and automated processing of huge archives of bioacoustic sound. Many of the tools previ- ously used in bioacoustics work well with small to medium-sized audio collections, but are challenged when processing large collections of tens of terabytes to petabyte size. In this thesis, a system is presented that assists researchers to listen to, view, anno- tate and run advanced audio feature extraction and machine learning algorithms on these audio recordings. This system is designed to scale to petabyte size. In addition, this system allows citizen scientists to participate in the process of annotating these large archives using a casual game metaphor. In this thesis, the use of this system to annotate a large audio archive called the Orchive will be evaluated. The Orchive contains over 20,000 hours of orca vocalizations collected over the course of 30 years, and represents one of the largest continuous collections of bioacoustic recordings in the world. The effectiveness of our semi-automatic approach for deriving knowledge from these recordings will be evaluated and results showing the utility of this system will be shown. / Graduate / 0984 / sness@sness.net

Online social media, such as news websites and community question answering (CQA) portals, have made useful information accessible to more people. However, many of online comment areas and communities are flooded with fraudulent information. These messages come from a special group of online users, called online paid posters, or termed "Internet water army" in China, represents a new type of online job opportunities. Online paid posters get paid for posting comments or articles on different online communities and websites for hidden purpose, e.g., to influence the opinion of other people towards certain social events or business markets. Though an interesting strategy in business marketing, paid posters may create a significant negative effect on the online communities, since the information from paid posters is usually not trustworthy. We thoroughly investigate the behavioral pattern of online paid posters based on a real-world trace data from the social comments of a business conflict. We design and validate a new detection mechanism, including both non-semantic analysis and semantic analysis, to identify potential online paid posters. Using supervised and unsupervised approaches, our test results with real-world datasets show a very promising performance. / Graduate

Neocortical circuits can adapt to changes in sensory input by modifying the strength or number of synapses. These changes have been well-characterized electrophysiologically in primary somatosensory (barrel) cortex of rodents across different ages and with different patterns of whisker stimulation. Previous work from our lab has identified layer-specific critical periods for synaptic potentiation after selective whisker experience (SWE), where all but one row of facial whiskers has been removed. Although whole-cell patch-clamp recording methods enable a mechanistic understanding of how synaptic plasticity can occur in vivo, they are painstakingly slow, typically focus on a small number of observed events, and are focused on a single pathway or restricted anatomical area. For example, most studies of plasticity in barrel cortex have focused on analyses of experience-dependent synaptic changes in layer 4 and layer 2/3, at a single time point, but it is unclear whether such changes are limited to these layers, or whether they persist over long time periods. Here we employ an established electron-microscopic technique that selectively intensifies synaptic contacts, in combination with unbiased, automated synapse detection, to broadly explore experience-dependent changes in synaptic size and density across many neocortical layers, regions, and time periods in a high-throughput fashion. To validate the method, we focused on imaging synaptic contacts at time points surrounding the critical period for strengthening of excitatory synapses in mouse barrel cortex, and compared these to electrophysiological analyses that show a doubling of synaptic events targeting layer 2/3 pyramidal neurons following SWE. We found that the pattern of occurrence of synapses across the cortical layers is significantly different following SWE. Also, an increase in length was observed specifically in layer 3 synapses. Furthermore, we uncovered potential bidirectional plasticity in L6 synapses depending on the developmental state of circuit and a potential critical period onset for L5A synapse at PND 18. The high resolution imaging and unbiased synapse detection has enabled us to potentially tease apart synaptic changes that occur in a laminar specific fashion. This high-throughput method will facilitate analysis of experience-dependent changes in synaptic density by age, sensory experience, genotype, pharmacological treatments or behavioral training, and will enable classification of synaptic structure to identify key parameters that can be changed by these variables.

The usage of 3D-modeling is expanding rapidly. Modeling from aerial imagery has become very popular due to its increasing number of both civilian and mili- tary applications like urban planning, navigation and target acquisition. This master thesis project was carried out at Vricon Systems at SAAB. The Vricon system produces high resolution geospatial 3D data based on aerial imagery from manned aircrafts, unmanned aerial vehicles (UAV) and satellites. The aim of this work was to investigate to what degree superpixel segmentation and supervised learning can be applied to a terrain classification problem using imagery and digital surface models (dsm). The aim was also to investigate how the height information from the digital surface model may contribute compared to the information from the grayscale values. The goal was to identify buildings, trees and ground. Another task was to evaluate existing methods, and compare results. The approach for solving the stated goal was divided into several parts. The first part was to segment the image using superpixel segmentation, after that features were extracted. Then the classifiers were created and trained and finally the classifiers were evaluated. The classification method that obtained the best results in this thesis had approx- imately 90 % correctly labeled superpixels. The result was equal, if not better, compared to other solutions available on the market.

This thesis introduces principled, broadly applicable, and efficient patch-based models for data processing applications. Recently, "epitomes" were introduced as patch-based probability models that are learned by compiling together a large number of examples of patches from input images. This thesis describes how epitomes can be used to model video data and a significant computational speedup is introduced that can be incorporated into the epitome inference and learning algorithm. In the case of videos, epitomes are estimated so as to model most of the small space-time cubes from the input data. Then, the epitome can be used for various modelling and reconstruction tasks, of which we show results for video super-resolution, video interpolation, and object removal. Besides computational efficiency, an interesting advantage of the epitome as a representation is that it can be reliably estimated even from videos with large amounts of missing data. This ability is illustrated on the task of reconstructing the dropped frames in a video broadcast using only the degraded video. Further, a new patch-based model is introduced, that when applied to epitomes, accounts for the varying geometric configurations of object features. The power of this model is illustrated on tasks such as multiple object registration and detection and missing data interpolation, including a difficult task of photograph relighting.

NASA‘s planned permanent return to the Moon by the year 2018 will demand advances in many technologies. Just as those pioneers who built a homestead in North America from abroad, it will be necessary to use the resources and materials available on the Moon, commonly referred to as in-situ resource utilization. In this concept study, we propose a role for autonomous, multirobot robotic precursor excavation missions that would prepare a lunar site for the arrival of astronauts, serving to establish methods of collecting oxygen, water and various other critical resources. A novel quantitative approach is presented that combines real-time 3D simulation with the use of Artificial Neural Tissues, a machine learning approach that produces autonomous controllers requiring little human supervision. Advantages of the autonomous multirobot approach to excavation over the traditional manually operated single vehicle ones are analyzed in terms of launch mass, power, efficiency, reliability, and overall mission cost.

A critical practical problem in the field of object recognition is an insufficient number of labeled training images, as manually labeling images is a time consuming task. For this reason, unsupervised learning techniques are used to take advantage of unlabeled training images to extract image representations that are useful for classification. However, unsupervised learning is in general difficult. We propose simplifying the unsupervised training problem considerably by taking the advance of motion information. The output of our method is a model that can generate a vector representation from any static image. However, the model is trained using images with additional motion information. To demonstrate the flobject analysis framework, we extend the latent Dirichlet allocation model to account for word-specific flow vectors. We show that the static image representations extracted using our model achieve higher classification rates and better generalization than standard topic models, spatial pyramid matching, and Gist descriptors.

Alternative splicing, the process by which a single gene may code for similar but different proteins, is an important process in biology, linked to development, cellular differentiation, genetic diseases, and more. Genome-wide analysis of alternative splicing patterns and regulation has been recently made possible due to new high throughput techniques for monitoring gene expression and genomic sequencing. This thesis introduces two algorithms for alternative splicing analysis based on large microarray and genomic sequence data. The algorithms, based on generative probabilistic models that capture structure and patterns in the data, are used to study global properties of alternative splicing. In the first part of the thesis, a microarray platform for monitoring alternative splicing is introduced. A spatial noise removal algorithm that removes artifacts and improves data fidelity is presented. The GenASAP algorithm (generative model for alternative splicing array platform) models the non-linear process in which targeted molecules bind to a microarray’s probes and is used to predict patterns of alternative splicing. Two versions of GenASAP have been developed. The first uses variational approximation to infer the relative amounts of the targeted molecules, while the second incorporates a more accurate noise and generative model and utilizes Markov chain Monte Carlo (MCMC) sampling. GenASAP, the first method to provide quantitative predictions of alternative splicing patterns on large scale data sets, is shown to generate useful and precise predictions based on independent RT-PCR validation (a slow but more accurate approach to measuring cellular expression patterns). In the second part of the thesis, the results obtained by GenASAP are analysed to reveal jointly regulated genes. The sequences of the genes are examined for potential regulatory factors binding sites using a new motif finding algorithm designed for this purpose. The motif finding algorithm, called GenBITES (generative model for binding sites) uses a fully Bayesian generative model for sequences, and the MCMC approach used for inference in the model includes moves that can efficiently create or delete motifs, and extend or contract the width of existing motifs. GenBITES has been applied to several synthetic and real data sets, and is shown to be highly competitive at a task for which many algorithms already exist. Although developed to analyze alternative splicing data, GenBITES outperforms most reported results on a benchmark data set based on transcription data.

NASA‘s planned permanent return to the Moon by the year 2018 will demand advances in many technologies. Just as those pioneers who built a homestead in North America from abroad, it will be necessary to use the resources and materials available on the Moon, commonly referred to as in-situ resource utilization. In this concept study, we propose a role for autonomous, multirobot robotic precursor excavation missions that would prepare a lunar site for the arrival of astronauts, serving to establish methods of collecting oxygen, water and various other critical resources. A novel quantitative approach is presented that combines real-time 3D simulation with the use of Artificial Neural Tissues, a machine learning approach that produces autonomous controllers requiring little human supervision. Advantages of the autonomous multirobot approach to excavation over the traditional manually operated single vehicle ones are analyzed in terms of launch mass, power, efficiency, reliability, and overall mission cost.

Post-transcriptional regulation is carried out by RNA-binding proteins (RBPs) that bind to specific RNA molecules and control their processing, localization, stability and degradation. Experimental studies have successfully identified RNA targets associated with specific RBPs. However, because the locations of the binding sites within the targets are unknown and because RBPs recognize both sequence and structure elements in their binding sites, identification of RBP binding preferences from these data remains challenging. The unifying theme of this thesis is to identify RBP binding preferences from experimental data. First, we propose a protocol to design a complex RNA pool that represents diverse sets of sequence and structure elements to be used in an in vitro assay to efficiently measure RBP binding preferences. This design has been implemented in the RNAcompete method, and applied genome-wide to human and Drosophila RBPs. We show that RNAcompete-derived motifs are consistent with established binding preferences. We developed two computational models to learn binding preferences of RBPs from large-scale data. Our first model, RNAcontext uses a novel representation of secondary structure to infer both sequence and structure preferences of RBPs, and is optimized for use with in vitro binding data on short RNA sequences. We show that including structure information improves the prediction accuracy significantly. Our second model, MaLaRKey, extends RNAcontext to fit motif models to sequences of arbitrary length, and to incorporate a richer set of structure features to better model in vivo RNA secondary structure. We demonstrate that MaLaRKey infers detailed binding models that accurately predict binding of full-length transcripts.