Positive natural selection leaves detectable, distinctive patterns in the genome in the form of a selective sweep. Identifying areas of the genome that have undergone selective sweeps is an area of high interest as it enables understanding of species and population evolution. Previous work has accomplished this by evaluating patterns within summary statistics computed across the genome and through application of machine learning techniques to raw population genomic data. When using raw population genomic data, convolutional neural networks have most recently been employed as they can handle large input arrays and maintain correlations among elements. Yet, such models often require massive amounts of training data and can be computationally expensive to train for a given problem. Instead, transfer learning has recently been used in the image analysis literature to improve machine learning models by learning the important features of images from large unrelated datasets beforehand, and then refining these models through subsequent application on smaller and more relevant datasets. We combine transfer learning with convolutional neural networks to improve classification of selective sweeps from raw population genomic data. We show that the combination of transfer learning with convolutional neural networks allows for accurate classification of selective sweeps. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

This thesis consists of studies of two important problems in transfer learning: binary classification under covariate-shift transfer, and off-policy evaluation in reinforcement learning. First, the problem of binary classification under covariate shift is considered, for which the first efficient procedure for optimal pruning of a dyadic classification tree is presented, where optimality is derived with respect to a notion of 𝒂𝒗𝒆𝒓𝒂𝒈𝒆 𝒅𝒊𝒔𝒄𝒓𝒆𝒑𝒂𝒏𝒄𝒚 between the shifted marginal distributions of source and target. Further, it is demonstrated that the procedure is adaptive to the discrepancy between marginal distributions in a neighbourhood of the decision boundary. It is shown how this notion of average discrepancy can be viewed as a measure of 𝒓𝒆𝒍𝒂𝒕𝒊𝒗𝒆 𝒅𝒊𝒎𝒆𝒏𝒔𝒊𝒐𝒏 between distributions, as it relates to existing notions of information such as the Minkowski and Renyi dimensions. Experiments are carried out on real data to verify the efficacy of the pruning procedure as compared to other baseline methods for pruning under transfer. The problem of off-policy evaluation for reinforcement learning is then considered, where two minimax lower bounds for the mean-square error of off-policy evaluation under Markov decision processes are derived. The first of these gives a non-asymptotic lower bound for OPE in finite state and action spaces over a model in which the mean reward is perturbed arbitrarily (up to a given magnitude) that depends on an average weighted chi-square divergence between the behaviour and target policies. The second provides an asymptotic lower bound for OPE in continuous state-space when the mean reward and policy ratio functions lie in a certain smoothness class. Finally, the results of a study that purported to have derived a policy for sepsis treatment in ICUs are replicated and shown to suffer from excessive variance and therefore to be unreliable; our lower bound is computed and used as evidence that reliable off-policy estimation from this data would have required a great deal more samples than were available.

Anwendung von maschinellen Lernverfahren (ML) in der Produktionstechnik, in Zeiten der Industrie 4.0, stark angestiegen. Insbesondere die Datenverfügbarkeit ist an dieser Stelle elementar und für die erfolgreiche Umsetzung einer ML-Applikation Voraussetzung. Falls für eine gegebene Problemstellung die Datenmenge oder -qualität nicht ausreichend ist, können Techniken, wie die Datenaugmentierung, der Einsatz von synthetischen Daten sowie das Transferlernen von ähnlichen Datensätzen Abhilfe schaffen. Innerhalb dieser Ausarbeitung wird das Konzept des Transferlernens im Bereich das Radial-Axial Ringwalzens (RAW) angewendet und am Beispiel der Zeitreihenprognose des Außendurchmessers über die Prozesszeit durchgeführt. Das Radial-Axial Ringwalzen ist ein warmumformendes Verfahren und dient der nahtlosen Ringherstellung.

Due to the increasing computing power and corresponding algorithms, the use of machine learning (ML) in production technology has risen sharply in the age of Industry 4.0. Data availability in particular is fundamental at this point and a prerequisite for the successful implementation of a ML application. If the quantity or quality of data is insufficient for a given problem, techniques such as data augmentation, the use of synthetic data and transfer learning of similar data sets can provide a remedy. In this paper, the concept of transfer learning is applied in the field of radial-axial ring rolling (rarr) and implemented using the example of time series prediction of the outer diameter over the process time. Radial-axial ring rolling is a hot forming process and is used for seamless ring production.

Nowadays, damage detection strategies built on the application of Artificial Neural Network tools to define models that mimic the dynamic behavior of structural systems are viral. However, a fundamental issue in developing these strategies for damage assessment is given by the unbalanced nature of the available databases for civil, mechanical, or aerospace applications, which commonly do not contain sufficient information from all the different classes that need to be identified. Unfortunately, when the aim is to classify between the healthy and damaged conditions in a structure or a generic dynamic system, it is extremely rare to have sufficient data for the unhealthy state since the system has already failed. At the same time, it is common to have plenty of data coming from the system under operational conditions. Consequently, the learning task, carried on with deep learning approaches, becomes case-dependent and tends to be specialized for a particular case and a very limited number of damage scenarios. This doctoral research presents a framework for damage classification in dynamic systems intended to overcome the limitations imposed by unbalanced datasets. In this methodology, the model's classification ability is enriched by using lower-level features derived through an improved extraction strategy that learns from a rich audio dataset how to characterize vibration traits starting from human voice recordings. This knowledge is then transferred to a target domain with much less data points, such as a structural system where the same discrimination approach is employed to classify and differentiate different health conditions. The goal is to enrich the model's ability to discriminate between classes on the audio records, presenting multiple different categories with more information to learn. The proposed methodology is validated both numerically and experimentally.

Neuroscience as a field is collecting more data than at any other time in history. The scale of this data allows us to ask fundamental questions about the mechanisms of brain function, the basis of behavior, and the development of disorders. Our ambitious goals as well as the abundance of data being recorded call for reproducible, reliable, and accessible systems to push the field forward. While we have made great strides in building reproducible and accessible machine learning (ML) systems for neuroscience, reliability remains a major issue. In this dissertation, we show that we can leverage existing data and domain expert knowledge to build more reliable ML systems to study animal behavior. First, we consider animal pose estimation, a crucial component in many scientific investigations. Typical transfer learning ML methods for behavioral tracking treat each video frame and object to be tracked independently. We improve on this by leveraging the rich spatial and temporal structures pervasive in behavioral videos. Our resulting weakly supervised models achieve significantly more robust tracking. Our tools allow us to achieve improved results when we have imperfect, limited data while requiring users to label fewer training frames and speeding up training. We can more accurately process raw video data and learn interpretable units of behavior. In turn, these improvements enhance performance on downstream applications. Next, we consider a ubiquitous approach to (attempt to) improve the reliability of ML methods, namely combining the predictions of multiple models, also known as deep ensembling. Ensembles of classical ML predictors, such as random forests, improve metrics such as accuracy by well-understood mechanisms such as improving diversity. However, in the case of deep ensembles, there is an open methodological question as to whether, given the choice between a deep ensemble and a single neural network with similar accuracy, one model is truly preferable over the other. Via careful experiments across a range of benchmark datasets and deep learning models, we demonstrate limitations to the purported benefits of deep ensembles. Our results challenge common assumptions regarding the effectiveness of deep ensembles and the “diversity” principles underpinning their success, especially with regards to important metrics for reliability, such as out-of-distribution (OOD) performance and effective robustness. We conduct additional studies of the effects of using deep ensembles when certain groups in the dataset are underrepresented (so-called “long tail” data), a setting whose importance in neuroscience applications is revealed by our aforementioned work. Altogether, our results demonstrate the essential importance of both holistic systems work and fundamental methodological work to understand the best ways to apply the benefits of modern machine learning to the unique challenges of neuroscience data analysis pipelines. To conclude the dissertation, we outline challenges and opportunities in building next-generation ML systems.

This dissertation comprises several studies addressing supervised learning problems where the supervision is imperfect. Firstly, we investigate the margin conditions in active learning. Active learning is characterized by its special mechanism where the learner can sample freely over the feature space and exploit mostly the limited labeling budget by querying the most informative labels. Our primary focus is to discern critical conditions under which certain active learning algorithms can outperform the optimal passive learning minimax rate. Within a non-parametric multi-class classification framework,our results reveal that the uniqueness of Bayes labels across the feature space serves as the pivotal determinant for the superiority of active learning over passive learning. Secondly, we study the estimation of central mean subspace (CMS), and its application in transfer learning. We show that a fast parametric convergence rate is achievable via estimating the expected smoothed gradient outer product, for a general class of covariate distribution that admits Gaussian or heavier distributions. When the link function is a polynomial with a degree of at most r and the covariates follow the standard Gaussian, we show that the prefactor depends on the ambient dimension d as d^r. Furthermore, we show that under a transfer learning setting, an oracle rate of prediction error as if the CMS is known is achievable, when the source training data is abundant. Finally, we present an innovative application involving the utilization of weak (noisy) labels for addressing an Individual Tree Crown (ITC) segmentation challenge. Here, the objective is to delineate individual tree crowns within a 3D LiDAR scan of tropical forests, with only 2D noisy manual delineations of crowns on RGB images available as a source of weak supervision. We propose a refinement algorithm designed to enhance the performance of existing unsupervised learning methodologies for the ITC segmentation problem.