Audio Inpainting

(c) 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Published version: IEEE Transactions on Audio, Speech and Language Processing 20(3): 922-932, Mar 2012. DOI: 10.1090/TASL.2011.2168211

In-silico investigation of the neonatal brain physiology using a systems biology approach: modelling birth asphyxia and neuroprotective strategies

Hypoxic ischaemic encephelopathy (HIE), often resulting from intrapartum hypoxic-ischemic injury, is a significant cause of death and morbidity before, during and after birth. In order to identify and monitor HIE, clinicians use non-invasive techniques including magnetic resonance spectroscopy (MRS) and near-infrared spectroscopy (NIRS). However, interpretation of these signals, particularly to determine the effectiveness of treatment and the severity of injury, is a challenging and difficult task. This thesis describes an attempt to use a systems biology approach to better understand the mechanisms behind HIE and its outcomes, using mathematical and computational techniques to analyse multimodal data, including broadband near-infrared spectroscopy (bNIRS). These models incorporate submodels of cerebral blood flow, oxygen transport and metabolism into a single cohesive model that attempts to simulate the observed measurements of tissue oxygenation and metabolism. The scope of this work is to both develop a set of computational tools that can be used to better understand existing systems biology models of the brain and to develop a new model which is able to incorporate the effects of therapeutic hypothermia, a common treatment for HIE, on the underlying physiology and its dynamics. The work begins by redeveloping the existing framework used for running and analysing systems biology models as used previously, before going on to develop a Bayesian framework which allows a better and more comprehensive interpretation of the results. This framework is then used to analyse three new models that incorporate the impact of therapeutic hypothermia on the piglet brain. The model determined to be most effective is then applied to clinical data from neonates that experience spontaneous desaturations in blood oxygen whilst undergoing hypothermic treatment. In all cases data from subjects with both mild and severe injuries are compared to determine if separate parameter spaces (and therefore physiological mechanisms) can be identified for each

Shadow removal utilizing multiplicative fusion of texture and colour features for surveillance image

Automated surveillance systems often identify shadows as parts of a moving object which jeopardized subsequent image processing tasks such as object identification and tracking. In this thesis, an improved shadow elimination method for an indoor surveillance system is presented. This developed method is a fusion of several image processing methods. Firstly, the image is segmented using the Statistical Region Merging algorithm to obtain the segmented potential shadow regions. Next, multiple shadow identification features which include Normalized Cross-Correlation, Local Color Constancy and Hue-Saturation-Value shadow cues are applied on the images to generate feature maps. These feature maps are used for identifying and removing cast shadows according to the segmented regions. The video dataset used is the Autonomous Agents for On-Scene Networked Incident Management which covers both indoor and outdoor video scenes. The benchmarking result indicates that the developed method is on-par with several normally used shadow detection methods. The developed method yields a mean score of 85.17% for the video sequence in which the strongest shadow is present and a mean score of 89.93% for the video having the most complex textured background. This research contributes to the development and improvement of a functioning shadow eliminator method that is able to cope with image noise and various illumination changes

Machine Learning As Tool And Theory For Computational Neuroscience

Computational neuroscience is in the midst of constructing a new framework for understanding the brain based on the ideas and methods of machine learning. This is effort has been encouraged, in part, by recent advances in neural network models. It is also driven by a recognition of the complexity of neural computation and the challenges that this poses for neuroscience’s methods. In this dissertation, I first work to describe these problems of complexity that have prompted a shift in focus. In particular, I develop machine learning tools for neurophysiology that help test whether tuning curves and other statistical models in fact capture the meaning of neural activity. Then, taking up a machine learning framework for understanding, I consider theories about how neural computation emerges from experience. Specifically, I develop hypotheses about the potential learning objectives of sensory plasticity, the potential learning algorithms in the brain, and finally the consequences for sensory representations of learning with such algorithms. These hypotheses pull from advances in several areas of machine learning, including optimization, representation learning, and deep learning theory. Each of these subfields has insights for neuroscience, offering up links for a chain of knowledge about how we learn and think. Together, this dissertation helps to further an understanding of the brain in the lens of machine learning

Connecting the Dots for People with Autism: A Data-driven Approach to Designing and Evaluating a Global Filter

Social communication is the use of language in social contexts. It encompasses social interaction, social cognition, pragmatics, and language processing” [3]. One presumed prerequisite of social communication is visual attention–the focus of this work. “Visual attention is a process that directs a tiny fraction of the information arriving at primary visual cortex to high-level centers involved in visual working memory and pattern recognition” [7]. This process involves the integration of two streams: the global and local streams; the global stream rapidly processes the scene, and the local stream processes details. This integration is important to social communication in that attending to both the global and local features of a scene are necessary to grasp the overall meaning. For people with autism spectrum disorder (ASD), the integration of these two streams can be disrupted by the tendency to privilege details (local processing) over seeing the big picture (global processing) [66]. Consequently, people with ASD may have challenges integrating visual attention, which may disrupt their social communication. This doctoral work explores the hypothesis that visual attention can be redirected to the features of an image that contain holistic information about a scene, which when highlighted might enable people with ASD to see the forest as well as the trees (i.e., seeing a scene as a whole rather than parts). The focuses are on 1) designing a global filter that can shift visual attention from local details to global features, and 2) evaluating the performance of a global filter by leveraging eye-tracking technology. This doctoral work manipulates visual stimuli in an effort to shift the visual attention of people with ASD. This doctoral work includes two development life cycles (i.e., design, develop, evaluate): 1) low-fidelity filter, and 2) high-fidelity filter. The low-fidelity filter life cycle includes the design of four low-fidelity filters for an initial experiment which was tested with an adult participant with ASD. The performance of each filter was evaluated by using verbal responses and eye-tracking data in terms of visual analysis, fixation analysis, and saccade analysis. The results from this cycle informed the decision for designing a high-fidelity filter in the next development life cycle. In this second cycle, ten children with ASD participated in the experiment. The performance of the high-fidelity filter was evaluated by using both verbal responses and eye-tracking data in terms of eye gaze behaviors. Results indicate that baseline conditions slightly outperform global filters in terms of verbal response and the eye gaze behaviors. To unpack the results in more details beyond group comparisons, three analyses (e.g., luminance, chroma, and spatial frequency) of image characteristics are performed to ascertain relevant aspects that contribute to the filter performance. The results indicate that there are no significant correlations between the image characteristics and the filter performance. However, among the three characteristics, spatial frequency is depicted as the most correlated factor with the filter performance. Additional analyses using neural networks, specifically Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN), are also explored. The result shows that CNN is more predictive of the relationship between an image and visual attention than MLP. This is a proof of concept that neural networks can be employed to identify images for future experiments, by avoiding any variance or bias in terms of unbalanced characteristics of images across the experimental image pool

Machine learning methods for sign language recognition: a critical review and analysis.

Sign language is an essential tool to bridge the communication gap between normal and hearing-impaired people. However, the diversity of over 7000 present-day sign languages with variability in motion position, hand shape, and position of body parts making automatic sign language recognition (ASLR) a complex system. In order to overcome such complexity, researchers are investigating better ways of developing ASLR systems to seek intelligent solutions and have demonstrated remarkable success. This paper aims to analyse the research published on intelligent systems in sign language recognition over the past two decades. A total of 649 publications related to decision support and intelligent systems on sign language recognition (SLR) are extracted from the Scopus database and analysed. The extracted publications are analysed using bibliometric VOSViewer software to (1) obtain the publications temporal and regional distributions, (2) create the cooperation networks between affiliations and authors and identify productive institutions in this context. Moreover, reviews of techniques for vision-based sign language recognition are presented. Various features extraction and classification techniques used in SLR to achieve good results are discussed. The literature review presented in this paper shows the importance of incorporating intelligent solutions into the sign language recognition systems and reveals that perfect intelligent systems for sign language recognition are still an open problem. Overall, it is expected that this study will facilitate knowledge accumulation and creation of intelligent-based SLR and provide readers, researchers, and practitioners a roadmap to guide future direction

Machine learning in solar physics

The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

Diversity across major and candidate genes in European local pig breeds

The aim of this work was to analyse the distribution of causal and candidate mutations associated to relevant productive traits in twenty local European pig breeds. Also, the potential of the SNP panel employed for elucidating the genetic structure and relationships among breeds was evaluated. Most relevant genes and mutations associated with pig morphological, productive, meat quality, reproductive and disease resistance traits were prioritized and analyzed in a maximum of 47 blood samples from each of the breeds (Alentejana, Apulo-Calabrese, Basque, Bísara, Majorcan Black, Black Slavonian (Crna slavonska), Casertana, Cinta Senese, Gascon, Iberian, Krskopolje (Krskopoljski), Lithuanian indigenous wattle, Lithuanian White Old Type, Mora Romagnola, Moravka, Nero Siciliano, Sarda, Schwa-bisch-Hallisches Schwein (Swabian Hall pig), Swallow-Bellied Mangalitsa and Turopolje). We successfully analyzed allelic variation in 39 polymorphisms, located in 33 candidate genes. Results provide relevant information regarding genetic diversity and segregation of SNPs associated to production and quality traits. Coat color and morphological trait-genes that show low level of segregation, and fixed SNPs may be useful for traceability. On the other hand, we detected SNPs which may be useful for association studies as well as breeding programs. For instance, we observed predominance of alleles that might be unfavorable for disease resistance and boar taint in most breeds and segregation of many alleles involved in meat quality, fatness and growth traits. Overall, these findings provide a detailed catalogue of segregating candidate SNPs in 20 European local pig breeds that may be useful for traceability purposes, for association studies and for breeding schemes. Population genetic analyses based on these candidate genes are able to uncover some clues regarding the hidden genetic substructure of these populations, as the extreme genetic closeness between Iberian and Alentejana breeds and an uneven admixture of the breeds studied. The results are in agreement with available knowledge regarding breed history and management, although largest panels of neutral markers should be employed to get a deeper understanding of the population’s structure and relationships