Gait recognition is important for identifying suspects in criminal investigations. This study will study the potential of using models based on transfer learning for this purpose. Both supervised and unsupervised learning will be examined. For the supervised learning part, the data is labeled and we investigate how accurate the models can be, and the impact of different walking conditions. Unsupervised learning is when the data is unlabeled and this part will determine if clustering can be used to identify groups of individuals without knowing who it is. Two deep learning models, the InceptionV3 model and the ResNet50V2, model are utilized, and the Gait Energy image method is used as gait representation. After optimization analysis, the models achieved the highest prediction accuracy of 100 percent when only including normal walking conditions and 99.25 percent when including different walking conditions such as carrying a backpack and wearing a coat, making them applicable for use in real-world investigations, provided that the data is labeled. Due to the apparent sensitivity of the models to varying camera angles, the clustering part resulted in an accuracy of approximately 30 percent. For unsupervised learning on gait recognition to be applicable in the real world, additional enhancements are required.

Chemosynthetic ecosystems are habitats whose food webs rely on chemosynthesis, a process by which bacteria fix carbon using energy from chemicals, rather than sunlight-driven photosynthesis for primary production, and they are found all over the world on the ocean floor. Although these deep-sea habitats are remote, they are increasingly being impacted by human activities such as oil and gas exploration and the imminent threat of deep-sea mining. My dissertation examines deep-sea chemosynthetic ecosystems at several ecological scales to answer basic biology questions and lay a foundation for future researchers studying these habitats. There are two major varieties of chemosynthetic ecosystems, hydrothermal vents and cold seeps, and my dissertation studies both. My first chapter begins at cold seeps and at the population level by modeling the population dynamics and lifespan of a single species of tubeworm, Escarpia laminata, found in the Gulf of Mexico. I found that this tubeworm, a foundation species that forms biogenic habitat for other seep animals, can reach ages over 300 years old, making it one of the longest-lived animals known to science. According to longevity theory, its extreme lifespan is made possible by the stable seep environment and lack of extrinsic mortality threats such as predation. My second chapter expands the scope of my research from this single species to the entire cold seep community and surrounding deep-sea animals common to the Gulf of Mexico. The chemicals released at cold seeps are necessary for chemosynthesis but toxic to non-adapted species such as cold-water corals. Community studies in this area have previously shown that seeps shape community assembly through niche processes. Using fine-scale water chemistry samples and photographic mapping of the seafloor, I found that depressed dissolved oxygen levels and the presence of hydrogen sulfide from seepage affect foundation taxa distributions, but the concentrations of hydrocarbons released from these seeps did not predict the distributions of corals or seep species. In my third chapter I examine seep community assembly drivers in the Costa Rica Margin and compare the macrofaunal composition at the family level to both hydrothermal vents and methane seeps around the world. The Costa Rica seep communities have not previously been described, and I found that depth was the primary driver behind community composition in this region. Although this margin is also home to a hybrid “hydrothermal seep” feature, this localized habitat did not have any discernible influence on the community samples analyzed. When vent and seep communities worldwide were compared at the family-level, geographic region was the greatest determinant of community similarity, accounting for more variation than depth and habitat type. Hydrothermal vent and methane seeps are two chemosynthetic ecosystems are created through completely different geological processes, leading to extremely different habitat conditions and distinct sets of related species. However, at the broadest spatial scale and family-level taxonomic resolution, neutral processes and dispersal limitation are the primary drivers behind community structure, moreso than whether the habitat is a seep or a vent. At more local spatial scales, the abiotic environment of seeps still has a significant influence on the ecology of deep-sea organisms. The millennial scale persistence of seeps in the Gulf of Mexico shapes the life history of vestimentiferan tubeworms, and the sulfide and oxygen concentrations at those seeps determine seep and non-seep species’ distributions across the deep seafloor. / Biology

Traditional Magnetic Resonance Imaging (MRI) reconstruction methods, which may be highly time-consuming and sensitive to noise, heavily depend on solving nonlinear optimization problems. By contrast, deep learning (DL)-based reconstruction methods do not need any explicit analytical data model and are robust to noise due to its large data-based training, which both make DL a versatile tool for fast and high-fidelity MR image reconstruction. While DL can be performed completely independently of traditional methods, it can, in fact, benefit from incorporating these established methods to achieve better results. To test this hypothesis, we proposed a hybrid DL-based MR image reconstruction method, which combines two state-of-the-art deep learning networks, U-Net and Generative Adversarial Network with Cycle loss (CycleGAN), with a traditional data reconstruction method: Projection Onto Convex Sets (POCS). Experiments were then performed to evaluate the method by comparing it to several existing state-of-the-art methods. Our results demonstrate that the proposed method outperformed the current state-of-the-art in terms of higher peak signal-to-noise ratio (PSNR) and higher Structural Similarity Index (SSIM). / Electrical and Computer Engineering

<p> Primnoa resedaeformis is a deep-sea gorgonian coral with worldwide distribution and a lifespan of at least several hundred years. Recent work has suggested that it may be possible to obtain extended, high-resolution records of ambient oceanographic conditions from Primnoa skeletons. This thesis focuses on specimens recently collected live from the Northeast Channel, SW of Nova Scotia, from depths of 300-500m. </p> <p> Skeletal microstructure was examined as a prerequisite to geochemical sampling. Skeletons exhibit periodic growth at three distinct scales. Concentric annual rings throughout the skeleton, and sub-annual laminae in the horny axis, measure 200 +1-100 microns and 15 +1-10 microns, respectively. Fine-scale striae in the outer calcite cortex measure 1.5 +1-2 microns. The dark, gorgonin-rich portion of annual rings in the horny axis forms in winter, when currents in the NE Channel are most energetic. Growth in these animals is apparently tied to the passage of currents at seasonal, lunar and tidal frequencies. Annual ring widths in the horny axis could not be successfully cross-dated, however, a prominent dark ring that appears to have been formed in 1976 is present in several of the colonies examined. Prominent dark rings may serve as useful benchmarks in sclerochronology. </p> <p> Mg/Ca and Sr/Ca were measured by laser ablation ICP-MS in the predominantly calcite axial cortex. Across a 1.5°C gradient, Mg/Ca is positively related to temperature. Sr/Ca also increases with temperature, but this may be explained by the influence of Mg/Ca on Sr partitioning, rather than temperature. Near annual-resolution timeseries profiles of Mg/Ca are consistent within and among colonies having different growth rates. Conversion of Mg/Ca profiles to temperatures using a provisional calibration [Mg/Ca (mmollmol) = 4.88(+/-1.09) T (°C) + 70.92 (+/-6.79)] yields a range of values and trends that are consistent with the observational data. Mg/Ca in Primnoa, therefore, is a viable means of monitoring bottom-water temperatures. The North Atlantic Oscillation (NAO) is responsible for a significant component of inter-annual temperature variability in the Scotia-Maine region. Mg/Ca records from older corals could therefore provide extended proxy records of the NAO. </p> / Thesis / Master of Science (MSc)

An experimental program was conducted to investigate the strength and deformations of deep beams reinforced with Carbon Fibre Reinforced Polymer (CFRP) longitudinal and transverse reinforcement. Two groups of beams were tested, with each group comprising three beams. Two of the three beams in each group were reinforced with CFRP bars while the third beam was reinforced with conventional rebars and the latter beam was used as a control specimen. Beams in group 1 had span-to-depth ratio of one, while those in group 2 had a span-to-depth ratio of two. Beams in both groups had height of 900 mm and width of 250 mm. All the beams were simply supported and were tested in four-point bending with the point loads applied at one-third of the span. The test results revealed no significant difference between the behavior of the FRP reinforced beams and the companion control beams. On the other hand due to lack of hooks at the ends of the CFRP bars, and the loss of bond between the CFRP fibres and the sand grains on the surface of the bar, the failure in the CFRP reinforced beams was caused by the loss of anchorage while in the steel reinforced beams, the failure was initiated by the yielding of the longitudinal steel, followed by the crushing of the horizontal compression strut, but the nodal zones did not fail in any of the beams. Consequently, it was concluded that CFRP reinforced deep beams could be designed using the current CSA method for conventional steel reinforced concrete deep beams, provided the anchorage or bond strength of FRP bars could be properly determined. The existing nodal efficiency factors for the CCC nodal zones, as given in the CSA A23.3. standard, could be applied to CFRP reinforced beams while the corresponding factor for the CCT zone may be conservatively assumed to be 0.68. Finally, despite the linear elastic behavior of CFRP reinforcement, deep beams reinforced with CFRP bars could be designed using strut and tie models. / Thesis / Master of Applied Science (MASc)

The abundance of next-generation sequencing (NGS) data has encouraged the adoption of machine learning methods to aid in the diagnosis and treatment of human disease. In particular, the last decade has shown the extensive use of predictive analytics in cancer research due to the prevalence of rich cellular descriptions of genetic and transcriptomic profiles of cancer cells. Despite the availability of wide-ranging forms of genomic data, few predictive models are designed to leverage multidimensional data sources. In this paper, we introduce a deep learning approach using neural network based information fusion to facilitate the integration of multi-platform genomic data, and the prediction of cancer cell sub-class. We propose the dGMU (deep gated multimodal unit), a series of multiplicative gates that can learn intermediate representations between multi-platform genomic data and improve cancer cell stratification. We also provide a framework for interpretable dimensionality reduction and assess several methods that visualize and explain the decisions of the underlying model. Experimental results on nine cancer types and four forms of NGS data (copy number variation, simple nucleotide variation, RNA expression, and miRNA expression) showed that the dGMU model improved the classification agreement of unimodal approaches and outperformed other fusion strategies in class accuracy. The results indicate that deep learning architectures based on multiplicative gates have the potential to expedite representation learning and knowledge integration in the study of cancer pathogenesis. / Thesis / Master of Science (MSc)

In this thesis we have present effective approaches for two classic Nature Language Processing tasks: Multi-label Text Classification(MLTC) and Sentiment Analysis(SA) based on two datasets. For MLTC, a robust deep learning approach based on convolution neural network(CNN) has been introduced. We have done this on almost one million records with a related label list consists of 20 labels. We have divided our data set into three parts, training set, validation set and test set. Our CNN based model achieved great result measured in F1 score. For SA, data set was more informative and well-structured compared with MLTC. A traditional word embedding method, Word2Vec was used for generating word vector of each text records. Following that, we employed several classic deep learning models such as Bi-LSTM, RCNN, Attention mechanism and CNN to extract sentiment features. In the next step, a classification frame was designed to graded. At last, the start-of-art language model, BERT which use transfer learning method was employed. In conclusion, we compared performance of RNN-based model, CNN-based model and pre-trained language model on classification task and discuss their applicability. / Thesis / Master of Science in Electrical and Computer Engineering (MSECE) / This theis purposed two deep learning solution to both multi-label classification problem and sentiment analysis problem.

This thesis aims at investigating the feasibility of realizing antireflection (AR) and high-reflection (HR) to the semiconductor waveguide end facet using monolithically integratable deep-etching technology to replace the conventional thin film dielectric coating counterpart. Conventional AR coating and HR coatings are the building blocks of semiconductor optical amplifier and semiconductor lasers. In this thesis, the AR coating and HR coating are first studied systematically and comprehensively using two computational electromagnetics approaches: plane wave transmission matrix method (TMM) and finite difference time domain (FDTD) method. The comparison of the results from the two approaches are made and discussed. A few concepts are clarified based on the different treatment between the AR coatings for bulk optics and those for semiconductor waveguide laser structure. The second part uses the same two numerical tools and more importantly, the knowledge gained from the first part to analyze and design deep-etched waveguide gratings for the advantage of ease of monolithic integration. A variational correction to the TMM is provided in order to consider effect of the finite etching depth also in the plane wave model. Specially, a new idea of achieving AR using deep-etched waveguide gratings is proposed and analyzed comprehensively. A preliminary design is obtained by TMM optimization and FDTD verifications, which provides a minimum power reflectivity in the order of 10-5 and a bandwidth of 45nm for the power reflectivity less than 10-3. In order to eliminate the nonphysical reflections from the boundary, the perfectly matched layer (PML) absorbing condition is employed and pre-tested for antireflection analysis. The effects of etching depth and number of etching grooves are specifically analyzed for the performance of proposed structures. Numerical results obtained by FDTD method indicate a promising potential for this alternative technologies. / Thesis / Master of Engineering (ME)

El análisis de los movimientos oculares constituye un importante desafío dada la gran cantidad de información presente en los mismos. Estos movimientos proveen numerosas claves para estudiar diversos procesos cognitivos considerando, entre otros aspectos, el modo y el tiempo en que se codi fica la información y qué parte de los datos obtenidos se usan o se ignoran. Avanzar en el entendimiento de los procesos involucrados en tareas de alta carga cognitiva puede ayudar en la detección temprana de enfermedades neurodegenerativas tales como el mal de Alzheimer o el de Parkinson. A su vez, la comprensión de estos procesos puede ampliar el abordaje de una gran variedad de temas vinculados con el modelado y control del sistema oculomotor humano. Durante el desarrollo de esta Tesis Doctoral se llevaron a cabo tres experimentos que utilizan técnicas de deep-learning y modelos lineales de efecto mixto a n de identi car patrones de movimiento ocular a partir del estudio de situaciones controladas. La primera experiencia tiene como objetivo diferenciar adultos mayores sanos de adultos mayores con posible enfermedad de Alzheimer, utilizando deep-learning con denoise-sparse-autoencoders y un clasifi cador, a partir de información del movimiento ocular durante la lectura. Los resultados obtenidos, con un 89;8% de efectividad en la clasi ficación por oración y 100% por sujeto, son satisfactorios. Esto sugiere que el uso de esta técnica es una alternativa factible para esta tarea. La segunda experiencia tiene como objetivo demostrar la factibilidad de la utilización de la dilatación de la pupila como un marcador cognitivo, en este caso mediante modelos lineales de efecto mixto. Los resultados indican que la dilatación se ve influenciada por la carga cognitiva, la semántica y las características específi cas de la oración, por lo que representa una alternativa viable para el análisis cognitivo. El tercero y último experimento tiene como objetivo comprobar la efectividad de la utilización de redes neuronales recurrentes, con unidades LSTM, para lograr una clasifi cación efectiva en rangos etarios correspondientes a jóvenes sanos y adultos mayores sanos, a partir del análisis de la dinámica de la pupila. Los resultados obtenidos demuestran que la utilización de esta técnica tiene un alto potencial en este campo logrando clasifi car jóvenes vs. adultos mayores con una efectividad media por oración de 76;99% y una efectividad media por sujeto del 90;24 %, utilizando información del ojo derecho o información binocular. Los resultados de estos estudios permiten afi rmar que la utilización de técnicas de deep learning, que no han sido exploradas para resolver problemas como los planteados utilizando eye-tracking, constituyen un gran área de interés. / TEXTO PARCIAL en período de teletrabajo

The deployment of Industrial Internet offers abundant passive data from manufacturing systems and networks, which enables data-driven modeling with high-data-demand, advanced statistical models such as Deep Neural Networks (DNNs). Deep Neural Networks (DNNs) have proven to be remarkably effective in supervised learning in critical manufacturing applications, such as AI-enabled automatic inspection, quality modeling, etc. However, there is a lack of performance guarantee of DNN models primarily due to data class imbalance, shifting distribution, multi-modality variables (e.g., time series and images) in training and testing datasets collected in manufacturing. Moreover, implementing these models on the manufacturing shop floor is difficult due to limitations in human-machine interaction. Inspired by active data generation through Design of Experiments (DoE) and passive observational data collection for manufacturing data analytics, we propose a SynthetIc Data gEneration and Sampling (SIDES) framework with a Graphical User Interface named SIDESync. This framework is designed to streamline SIDES execution within manufacturing environments, to provide adequate DNN model performance through the improvement of training data preparation and enhancing human-machine interaction. In the SIDES framework, a bi-level Hierarchical Contextual Bandits is proposed to provide a scientific way to integrate DoE and observational data sampling, which optimizes DNNs' online learning performance. Multimodality-aligned variational Autoencoder transforms the multimodal predictors from manufacturing into a shared low-dimensional latent space for controlled data generation from DoE and effective sampling from observational data. The SIDESync Graphical User Interface (GUI), developed using the Streamlit library in Python, simplifies the configuration, monitoring, and analysis of SIDES experiments. This streamlined approach facilitates access to the SIDES framework and enhances human-machine interaction capabilities. The merits of SIDES are evaluated by a real case study of printed electronics with a binary multimodal data classification problem. Results show the advantages of the cost-effective integration of DoE in improving the DNNs' online learning performance. / Master of Science / The Industrial Internet's growth has brought in a massive amount of data from manufacturing systems leading to advanced data analysis methods using techniques like Deep Neural Networks (DNNs). These powerful models have shown great promise in critical manufacturing tasks, such as AI-driven quality control. However, challenges remain in ensuring these models perform well. For example, the lack of good data results in models with poor performance. Furthermore, deploying these models on the manufacturing shop floor poses challenges due to limited human-machine interaction capabilities. To tackle these challenges, we introduce the SynthetIc Data gEneration and Sampling (SIDES) framework with a user-friendly interface called SIDESync to enhance the human-machine interaction. This framework will improve how training data is prepared, ultimately boosting the performance of DNN models. Within this framework, we proposed a method called bi-level Hierarchical Contextual Bandits that combines real-world data sampling with a technique called Design of Experiments (DoE) to help Deep Neural Networks (DNNs) learn more effectively as they operate. We also used a tool called a Multimodality-Aligned Variational Autoencoder, which helps convert various types of manufacturing data (like sensor readings and images) into a standard format. This conversion makes it easier to generate new data from experiments and efficiently use real-world data samples. The SIDESync Graphical User Interface (GUI) is created using Python's Streamlit library. It makes setting up, monitoring, and analyzing SIDES experiments much easier. This user-friendly system improves access to the SIDES framework and boosts interactions between humans and machines. To prove how effective SIDES is, we conducted a real case study of data collected from printed electronics manufacturing. We focused on a problem where we needed to classify the final product quality using in-situ data with DNN model prediction. Our results clearly showed that integrating DoE improved how DNNs learned online, all while keeping costs in check. This work opens up exciting possibilities for making data-driven decisions in manufacturing smarter and more efficient.