4,672 research outputs found
Techniques for augmenting the visualisation of dynamic raster surfaces
Despite their aesthetic appeal and condensed nature, dynamic raster surface representations such as a temporal series of a landform and an attribute series of a socio-economic attribute of an area, are often criticised for the lack of an effective information delivery and interactivity.In this work, we readdress some of the earlier raised reasons for these limitations -information-laden quality of surface datasets, lack of spatial and temporal continuity in the original data, and a limited scope for a real-time interactivity. We demonstrate with examples that the use of four techniques namely the re-expression of the surfaces as a framework of morphometric features, spatial generalisation, morphing, graphic lag and brushing can augment the visualisation of dynamic raster surfaces in temporal and attribute series
Doctor of Philosophy
dissertationDataflow pipeline models are widely used in visualization systems. Despite recent advancements in parallel architecture, most systems still support only a single CPU or a small collection of CPUs such as a SMP workstation. Even for systems that are specifically tuned towards parallel visualization, their execution models only provide support for data-parallelism while ignoring taskparallelism and pipeline-parallelism. With the recent popularization of machines equipped with multicore CPUs and multi-GPU units, these visualization systems are undoubtedly falling further behind in reaching maximum efficiency. On the other hand, there exist several libraries that can schedule program executions on multiple CPUs and/or multiple GPUs. However, due to differences in executing a task graph and a pipeline along with their APIs being considerably low-level, it still remains a challenge to integrate these run-time libraries into current visualization systems. Thus, there is a need for a redesigned dataflow architecture to fully support and exploit the power of highly parallel machines in large-scale visualization. The new design must be able to schedule executions on heterogeneous platforms while at the same time supporting arbitrarily large datasets through the use of streaming data structures. The primary goal of this dissertation work is to develop a parallel dataflow architecture for streaming large-scale visualizations. The framework includes supports for platforms ranging from multicore processors to clusters consisting of thousands CPUs and GPUs. We achieve this in our system by introducing the notion of Virtual Processing Elements and Task-Oriented Modules along with a highly customizable scheduler that controls the assignment of tasks to elements dynamically. This creates an intuitive way to maintain multiple CPU/GPU kernels yet still provide coherency and synchronization across module executions. We have implemented these techniques into HyperFlow which is made of an API with all basic dataflow constructs described in the dissertation, and a distributed run-time library that can be used to deploy those pipelines on multicore, multi-GPU and cluster-based platforms
DEPLOYING, IMPROVING AND EVALUATING EDGE BUNDLING METHODS FOR VISUALIZING LARGE GRAPHS
A tremendous increase in the scale of graphs has been witnessed in a wide range of fields, which demands efficient and effective visualization techniques to assist users in better understandings of large graphs. Conventional node-link diagrams are often used to visualize graphs, whereas excessive edge crossings can easily incur severe visual clutter in the node-link diagram of a large graph. Edge bundling can effectively remedy visual clutter and reveal high-level graph structures. Although significant efforts have been devoted to developing edge bundling, three challenging problems remain. First, edge bundling techniques are often computationally expensive and are not easy to deploy for web-based applications. The state-of-the-art edge bundling methods often require special system supports and techniques such as high-end GPU acceleration for large graphs, which makes these methods less portable, especially for ubiquitous mobile devices. Second, the quantitative quality of edge bundling results is barely assessed in the literature. Currently, the comparison of edge bundling mainly focuses on computational performance and perceptual results. Third, although the family of edge bundling techniques has a rich set of bundling layout, there is a lack of a generic method to generate different styles of edge bundling.
In this research, I aim to address these problems and have made the following contributions. First, I provide an efficient framework to deploy edge bundling for web-based platforms by exploiting standard graphics hardware functions and libraries. My framework can generate high-quality edge bundling results on web-based platforms, and achieve a speedup of 50X compared to the previous state-of-the-art edge bundling method on a graph with half of a million edges. Second, I propose a new moving least squares based approach to lower the algorithm complexity of edge bundling. In addition, my approach can generate better bundling results compared to other methods based on a quality metric. Third, I provide an information-theoretic metric to evaluate the edge bundling methods. I leverage information theory in this metric. With my information-theoretic metric, domain users can choose appropriate edge bundling methods with proper parameters for their applications. Last but not least, I present a deep learning framework for edge bundling visualizations. Through a training process that learns the results of a specific edge bundling method, my deep learning framework can infer the final layout of the edge bundling method. My deep learning framework is a generic framework that can generate the corresponding results of different edge bundling methods.
Adviser: Hongfeng Y
Methods to study splicing from high-throughput RNA Sequencing data
The development of novel high-throughput sequencing (HTS) methods for RNA
(RNA-Seq) has provided a very powerful mean to study splicing under multiple
conditions at unprecedented depth. However, the complexity of the information
to be analyzed has turned this into a challenging task. In the last few years,
a plethora of tools have been developed, allowing researchers to process
RNA-Seq data to study the expression of isoforms and splicing events, and their
relative changes under different conditions. We provide an overview of the
methods available to study splicing from short RNA-Seq data. We group the
methods according to the different questions they address: 1) Assignment of the
sequencing reads to their likely gene of origin. This is addressed by methods
that map reads to the genome and/or to the available gene annotations. 2)
Recovering the sequence of splicing events and isoforms. This is addressed by
transcript reconstruction and de novo assembly methods. 3) Quantification of
events and isoforms. Either after reconstructing transcripts or using an
annotation, many methods estimate the expression level or the relative usage of
isoforms and/or events. 4) Providing an isoform or event view of differential
splicing or expression. These include methods that compare relative
event/isoform abundance or isoform expression across two or more conditions. 5)
Visualizing splicing regulation. Various tools facilitate the visualization of
the RNA-Seq data in the context of alternative splicing. In this review, we do
not describe the specific mathematical models behind each method. Our aim is
rather to provide an overview that could serve as an entry point for users who
need to decide on a suitable tool for a specific analysis. We also attempt to
propose a classification of the tools according to the operations they do, to
facilitate the comparison and choice of methods.Comment: 31 pages, 1 figure, 9 tables. Small corrections adde
Overview on agent-based social modelling and the use of formal languages
Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft
Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity
The clustering of data into physically meaningful subsets often requires
assumptions regarding the number, size, or shape of the subgroups. Here, we
present a new method, simultaneous coherent structure coloring (sCSC), which
accomplishes the task of unsupervised clustering without a priori guidance
regarding the underlying structure of the data. sCSC performs a sequence of
binary splittings on the dataset such that the most dissimilar data points are
required to be in separate clusters. To achieve this, we obtain a set of
orthogonal coordinates along which dissimilarity in the dataset is maximized
from a generalized eigenvalue problem based on the pairwise dissimilarity
between the data points to be clustered. This sequence of bifurcations produces
a binary tree representation of the system, from which the number of clusters
in the data and their interrelationships naturally emerge. To illustrate the
effectiveness of the method in the absence of a priori assumptions, we apply it
to three exemplary problems in fluid dynamics. Then, we illustrate its capacity
for interpretability using a high-dimensional protein folding simulation
dataset. While we restrict our examples to dynamical physical systems in this
work, we anticipate straightforward translation to other fields where existing
analysis tools require ad hoc assumptions on the data structure, lack the
interpretability of the present method, or in which the underlying processes
are less accessible, such as genomics and neuroscience
A Framework for Executable Systems Modeling
Systems Modeling Language (SysML), like its parent language, the Unified Modeling Language (UML), consists of a number of independently derived model languages (i.e. state charts, activity models etc.) which have been co-opted into a single modeling framework. This, together with the lack of an overarching meta-model that supports uniform semantics across the various diagram types, has resulted in a large unwieldy and informal language schema. Additionally, SysML does not offer a built in framework for managing time and the scheduling of time based events in a simulation.
In response to these challenges, a number of auxiliary standards have been offered by the Object Management Group (OMG); most pertinent here are the foundational UML subset (fUML), Action language for fUML (Alf), and the UML profile for Modeling and Analysis of Real Time and Embedded Systems (MARTE). However, there remains a lack of a similar treatment of SysML tailored towards precise and formal modeling in the systems engineering domain. This work addresses this gap by offering refined semantics for SysML akin to fUML and MARTE standards, aimed at primarily supporting the development of time based simulation models typically applied for model verification and validation in systems engineering.
The result of this work offers an Executable Systems Modeling Language (ESysML) and a prototype modeling tool that serves as an implementation test bed for the ESysML language. Additionally a model development process is offered to guide user appropriation of the provided framework for model building
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed
appropriately, may deliver the best of expectations over many application sectors across the field. For this
to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability,
an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural
Networks) that were not present in the last hype of AI (namely, expert systems and rule based models).
Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely
acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in
this article examines the existing literature and contributions already done in the field of XAI, including a
prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define
explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that
covers such prior conceptual propositions with a major focus on the audience for which the explainability
is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions
related to the explainability of different Machine Learning models, including those aimed at explaining
Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This
critical literature analysis serves as the motivating background for a series of challenges faced by XAI,
such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept
of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI
methods in real organizations with fairness, model explainability and accountability at its core. Our
ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve
as reference material in order to stimulate future research advances, but also to encourage experts and
professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any
prior bias for its lack of interpretability.Basque GovernmentConsolidated Research Group MATHMODE - Department of Education of the Basque Government IT1294-19Spanish GovernmentEuropean Commission TIN2017-89517-PBBVA Foundation through its Ayudas Fundacion BBVA a Equipos de Investigacion Cientifica 2018 call (DeepSCOP project)European Commission 82561
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