Iterative Reconstrained Low-rank Representation via Weighted Nonconvex Regularizer

OAPA Benefiting from the joint consideration of geometric structures and low-rank constraint, graph low-rank representation (GLRR) method has led to the state-of-the-art results in many applications. However, it faces the limitations that the structure of errors should be known a prior, the isolated construction of graph Laplacian matrix, and the over shrinkage of the leading rank components. To improve GLRR in these regards, this paper proposes a new LRR model, namely iterative reconstrained LRR via weighted nonconvex regularization (IRWNR), using three distinguished properties on the concerned representation matrix. The first characterizes various distributions of the errors into an adaptively learned weight factor for more flexibility of noise suppression. The second generates an accurate graph matrix from weighted observations for less afflicted by noisy features. The third employs a parameterized Rational function to reveal the importance of different rank components for better approximation to the intrinsic subspace structure. Following a deep exploration of automatic thresholding, parallel update, and partial SVD operation, we derive a computationally efficient low-rank representation algorithm using an iterative reconstrained framework and accelerated proximal gradient method. Comprehensive experiments are conducted on synthetic data, image clustering, and background subtraction to achieve several quantitative benchmarks as clustering accuracy, normalized mutual information, and execution time. Results demonstrate the robustness and efficiency of IRWNR compared with other state-of-the-art models

Resolving Biological Trajectories in Single-cell Data using Feature Selection and Multi-modal Integration

Single-cell technologies can readily measure the expression of thousands of molecular features from individual cells undergoing dynamic biological processes, such as cellular differentiation, immune response, and disease progression. While computational trajectory inference methods and RNA velocity approaches have been developed to study how subtle changes in gene or protein expression impact cell fate decision-making, identifying characteristic features that drive continuous biological processes remains difficult to detect due to the inherent biological or technical challenges associated with single-cell data. Here, we developed two data representation-based approaches for improving inference of cellular dynamics. First, we present DELVE, an unsupervised feature selection method for identifying a representative subset of dynamically-expressed molecular features that resolve cellular trajectories in noisy data. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effect of unwanted sources of variation confounding inference and models cell states from dynamic feature modules that constitute core regulatory complexes. Using simulations, single-cell RNA sequencing data, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate that DELVE selects genes or proteins that more accurately characterize cell populations and improve the recovery of cell type transitions. Next, we present the first task-oriented benchmarking study that investigates integration of temporal gene expression modalities for dynamic cell state prediction. We benchmark ten multi-modal integration approaches on ten datasets spanning different biological contexts, sequencing technologies, and species. This study illustrates how temporal gene expression modalities can be optimally combined to improve inference of cellular trajectories and more accurately predict sample-associated perturbation and disease phenotypes. Lastly, we illustrate an application of these approaches and perform an integrative analysis of gene expression and RNA velocity data to study the crosstalk between signaling pathways that govern the mesendoderm fate decision during directed definitive endoderm differentiation. Results of this study suggest that lineage-specific, temporally expressed genes within the primitive streak may serve as a potential target for increasing definitive endoderm efficiency. Collectively, this work uses scalable data-driven approaches to effectively manage the inherent biological or technical challenges associated with single-cell data in order to improve inference of cellular dynamics.Doctor of Philosoph

Efficient Numerical Solution of Large Scale Algebraic Matrix Equations in PDE Control and Model Order Reduction

Matrix Lyapunov and Riccati equations are an important tool in mathematical systems theory. They are the key ingredients in balancing based model order reduction techniques and linear quadratic regulator problems. For small and moderately sized problems these equations are solved by techniques with at least cubic complexity which prohibits their usage in large scale applications. Around the year 2000 solvers for large scale problems have been introduced. The basic idea there is to compute a low rank decomposition of the quadratic and dense solution matrix and in turn reduce the memory and computational complexity of the algorithms. In this thesis efficiency enhancing techniques for the low rank alternating directions implicit iteration based solution of large scale matrix equations are introduced and discussed. Also the applicability in the context of real world systems is demonstrated. The thesis is structured in seven central chapters. After the introduction chapter 2 introduces the basic concepts and notations needed as fundamental tools for the remainder of the thesis. The next chapter then introduces a collection of test examples spanning from easily scalable academic test systems to badly conditioned technical applications which are used to demonstrate the features of the solvers. Chapter four and five describe the basic solvers and the modifications taken to make them applicable to an even larger class of problems. The following two chapters treat the application of the solvers in the context of model order reduction and linear quadratic optimal control of PDEs. The final chapter then presents the extensive numerical testing undertaken with the solvers proposed in the prior chapters. Some conclusions and an appendix complete the thesis

Comparison of Latent-Space Generative Models through Statistics and Mapping

Although data generation is a task with broad and exciting applications, samples created by generative models often fall victim to reduced variability and biases which, when coupled with the lack of explainability common to all neural networks, makes the evaluation of issues and limitations of these systems challenging. Much effort has been devoted to the exploration of the latent spaces of generative models in order to find more controllable editing directions and to the idea that better models would produce more disentangled representations. In this thesis we present a detailed and comparative analysis of latent-space generative models, beginning from their theoretical foundation and up to a number of statistical and empirical findings. We show that the original data is the sole factor truly impacting how different generative models learn, more than one may imagine: under the same dataset, even very different architectures distribute their latent spaces in essentially the same way. These results suggest new directions of research for representation learning, with the potential to transfer acquired knowledge between models and to understand the common mechanisms behind learning as a whole. Parts of the topics discussed in this thesis are a joint work, particularly those related to the mappings between models; they have already seen publication as a paper

Occluder-aided non-line-of-sight imaging

Non-line-of-sight (NLOS) imaging is the inference of the properties of objects or scenes outside of the direct line-of-sight of the observer. Such inferences can range from a 2D photograph-like image of a hidden area, to determining the position, motion or number of hidden objects, to 3D reconstructions of a hidden volume. NLOS imaging has many enticing potential applications, such as leveraging the existing hardware in many automobiles to identify hidden pedestrians, vehicles or other hazards and hence plan safer trajectories. Other potential application areas include improving navigation for robots or drones by anticipating occluded hazards, peering past obstructions in medical settings, or in surveying unreachable areas in search-and-rescue operations. Most modern NLOS imaging methods fall into one of two categories: active imaging methods that have some control of the illumination of the hidden area, and passive methods that simply measure light that already exists. This thesis introduces two NLOS imaging methods, one of each category, along with modeling and data processing techniques that are more broadly applicable. The methods are linked by their use of objects (‘occluders’) that reside somewhere between the observer and the hidden scene and block some possible light paths. Computational periscopy, a passive method, can recover the unknown position of an occluding object in the hidden area and then recover an image of the hidden scene behind it. It does so using only a single photograph of a blank relay wall taken by an ordinary digital camera. We develop also a framework using an optimized preconditioning matrix to improve the speed at which these reconstructions can be made and greatly improve the robustness to ambient light. Lastly, we develop tools necessary to demonstrate recovery of scenes at multiple unknown depths – paving the way towards three-dimensional reconstructions. Edge-resolved transient imaging, an active method, enables the formation of 2.5D representations – a plan view plus heights – of large-scale scenes. A pulsed laser illuminates spots along a small semi-circle on the floor, centered on the edge of a vertical wall such as in a doorway. The wall edge occludes some light paths, only allowing the laser light reflecting off of the floor to illuminate certain portions of the hidden area beyond the wall, depending on where along the semi-circle it is illuminating. The time at which photons return following a laser pulse is recorded. The occluding wall edge provides angular resolution, and time-resolved sensing provides radial resolution. This novel acquisition strategy, along with a scene response model and reconstruction algorithm, allow for 180° field of view reconstructions of large-scale scenes unlike other active imaging methods. Lastly, we introduce a sparsity penalty named mutually exclusive group sparsity (MEGS), that can be used as a constraint or regularization in optimization problems to promote solutions in which certain components are mutually exclusive. We explore how this penalty relates to other similar penalties, develop fast algorithms to solve MEGS-regularized problems, and demonstrate how enforcing mutual exclusivity structure can provide great utility in NLOS imaging problems

Applications of Artificial Intelligence in Medicine Practice

This book focuses on a variety of interdisciplinary perspectives concerning the theory and application of artificial intelligence (AI) in medicine, medically oriented human biology, and healthcare. The list of topics includes the application of AI in biomedicine and clinical medicine, machine learning-based decision support, robotic surgery, data analytics and mining, laboratory information systems, and usage of AI in medical education. Special attention is given to the practical aspect of a study. Hence, the inclusion of a clinical assessment of the usefulness and potential impact of the submitted work is strongly highlighted

Understanding transcriptional regulation through computational analysis of single-cell transcriptomics

Gene expression is tightly regulated by complex transcriptional regulatory mechanisms to achieve specific expression patterns, which are essential to facilitate important biological processes such as embryonic development. Dysregulation of gene expression can lead to diseases such as cancers. A better understanding of the transcriptional regulation will therefore not only advance the understanding of fundamental biological processes, but also provide mechanistic insights into diseases. The earlier versions of high-throughput expression profiling techniques were limited to measuring average gene expression across large pools of cells. In contrast, recent technological improvements have made it possible to perform expression profiling in single cells. Single-cell expression profiling is able to capture heterogeneity among single cells, which is not possible in conventional bulk expression profiling. In my PhD, I focus on developing new algorithms, as well as benchmarking and utilising existing algorithms to study the transcriptomes of various biological systems using single-cell expression data. I have developed two different single-cell specific network inference algorithms, BTR and SPVAR, which are based on two different formalisms, Boolean and autoregression frameworks respectively. BTR was shown to be useful for improving existing Boolean models with single-cell expression data, while SPVAR was shown to be a conservative predictor of gene interactions using pseudotime-ordered single-cell expression data. In addition, I have obtained novel biological insights by analysing single-cell RNAseq data from the epiblast stem cells reprogramming and the leukaemia systems. Three different driver genes, namely Esrrb, Klf2 and GY118F, were shown to drive reprogramming of epiblast stem cells via different reprogramming routes. As for the leukaemia system, FLT3-ITD and IDH1-R132H mutations were shown to interact with each other and potentially predispose some cells for developing acute myeloid leukaemia.Wellcome Trust and Cambridge Trus

The functional anatomy of white matter pathways for visual configuration learning

The role of the medial temporal lobes (MTL) in visuo-spatial learning has been extensively studied and documented in the neuroscientific literature. Numerous animal and human studies have demonstrated that the parahippocampal place area (PPA), which sits at the confluence of the parahippocampal and lingual gyri, is particularly important for learning the spatial configuration of objects in visually presented scenes. In current visuo-spatial processing models, the PPA sits downstream from the parietal lobes which are involved in multiple facets of spatial processing. Yet, direct input to the PPA from early visual cortex (EVC) is rarely discussed and poorly understood. This thesis adopted a multimodal neuroimaging analysis approach to study the functional anatomy of these connections. First, the pattern of structural connectivity between EVC and the MTL was explored by means of surface-based ‘connectomes’ constructed from diffusion MRI tractography in a cohort of 200 healthy young adults from the Human Connectome Project. Through this analysis, the PPA emerged as a primary recipient of EVC connections within the MTL. Second, a data-driven clustering analysis of the PPA’s connectivity to an extended cortical region (including EVC, retrosplenial cortex, and other areas) revealed multiple clusters with different connectivity profiles within the PPA. The two main clusters were located in the posterior and anterior portions of the PPA, with the posterior cluster preferentially connected to EVC. Motivated by this result, virtual tractography dissections were used to delineate the medial occipital longitudinal tract (MOLT), the white matter bundle connecting the PPA with EVC. The properties of this bundle and its relation to visual configuration learning were verified in a different, cross-sectional adult cohort of 90 subjects. Finally, the role of the MOLT in the visuo-spatial learning domain was further confirmed in the case of a stroke patient who, after bilateral occipital injury, exhibited deficits confined to this domain. The results presented in this work suggest that the MOLT should be included in current visuo-spatial processing models as it offers additional insight into how the MTL acquires and processes information for spatial learning

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf