A Review and Characterization of Progressive Visual Analytics

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions

Procedural Generation and Rendering of Realistic, Navigable Forest Environments: An Open-Source Tool

Simulation of forest environments has applications from entertainment and art creation to commercial and scientific modelling. Due to the unique features and lighting in forests, a forest-specific simulator is desirable, however many current forest simulators are proprietary or highly tailored to a particular application. Here we review several areas of procedural generation and rendering specific to forest generation, and utilise this to create a generalised, open-source tool for generating and rendering interactive, realistic forest scenes. The system uses specialised L-systems to generate trees which are distributed using an ecosystem simulation algorithm. The resulting scene is rendered using a deferred rendering pipeline, a Blinn-Phong lighting model with real-time leaf transparency and post-processing lighting effects. The result is a system that achieves a balance between high natural realism and visual appeal, suitable for tasks including training computer vision algorithms for autonomous robots and visual media generation.Comment: 14 pages, 11 figures. Submitted to Computer Graphics Forum (CGF). The application and supporting configuration files can be found at https://github.com/callumnewlands/ForestGenerato

Mulsemedia: State of the art, perspectives, and challenges

Mulsemedia-multiple sensorial media-captures a wide variety of research efforts and applications. This article presents a historic perspective on mulsemedia work and reviews current developments in the area. These take place across the traditional multimedia spectrum-from virtual reality applications to computer games-as well as efforts in the arts, gastronomy, and therapy, to mention a few. We also describe standardization efforts, via the MPEG-V standard, and identify future developments and exciting challenges the community needs to overcome

Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption

We argue that the vulnerability of model parameters is of crucial value to the study of model robustness and generalization but little research has been devoted to understanding this matter. In this work, we propose an indicator to measure the robustness of neural network parameters by exploiting their vulnerability via parameter corruption. The proposed indicator describes the maximum loss variation in the non-trivial worst-case scenario under parameter corruption. For practical purposes, we give a gradient-based estimation, which is far more effective than random corruption trials that can hardly induce the worst accuracy degradation. Equipped with theoretical support and empirical validation, we are able to systematically investigate the robustness of different model parameters and reveal vulnerability of deep neural networks that has been rarely paid attention to before. Moreover, we can enhance the models accordingly with the proposed adversarial corruption-resistant training, which not only improves the parameter robustness but also translates into accuracy elevation.Comment: Accepted by AAAI 202

Um Modelo para a visualização de conhecimento baseado em imagens semânticas

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia e Gestão do ConhecimentoOs avanços no processamento e gerenciamento eletrônico de documentos têm gerado um acúmulo grande de conhecimento que tem excedido o que os usuários comuns podem perceber. Uma quantidade considerável de conhecimento encontra-se explicitado em diversos documentos armazenados em repositórios digitais. Em muitos casos, a possibilidade de acessar de forma eficiente e reutilizar este conhecimento é limitada. Como resultado disto, a maioria do conhecimento não é suficientemente explorado nem compartilhado, e conseqüentemente é esquecido em um tempo relativamente curto. As tecnologias emergentes de visualização e o sistema perceptual humano podem ser explorados para melhorar o acesso a grandes espaços de informação facilitando a detecção de padrões. Por outro lado, o uso de elementos visuais que contenham representações do mundo real que a priori são conhecidos pelo grupo-alvo e que fazem parte da sua visão de mundo, permite que o conhecimento apresentado por meio destas representações possa facilmente ser relacionados com o conhecimento prévio dos indivíduos, facilitando assim a aprendizagem. Apesar das representações visuais terem sido usadas como suporte para a disseminação do conhecimento, não têm sido propostos modelos que integrem os métodos e técnicas da engenharia do conhecimento com o uso das imagens como meio para recuperar e visualizar conhecimento. Neste trabalho apresenta-se um modelo que visa facilitar a visualização do conhecimento armazenado em repositórios digitais usando imagens semânticas. O usuário, através das imagens semânticas, pode recuperar e visualizar o conhecimento relacionado às entidades representadas nas regiões das imagens. As imagens semânticas são representações visuais do mundo real as quais são conhecidas previamente pelo grupo alvo e possuem mecanismos que permitem identificar os conceitos do domínio representados em cada região. O modelo proposto apóia-se no framework para visualização do conhecimento proposto por Burkhard e descreve as interações dos usuários com as imagens. Um protótipo foi desenvolvido para demonstrar a viabilidade do modelo usando imagens no domínio da anatomia, a Foundational Model of Anatomy e a Unified Medical Language System como conhecimento do domínio e o banco de dados da Scientific Electronic Library Online como repositório de documento.Advances in processing and electronic document management have generated a great accumulation of knowledge that is beyond what ordinary users can understand. A considerable amount of knowledge is explained in various documents stored in digital repositories. In many cases, the ability to eficiently access and reuse this knowledge is limited. As a result, most knowledge is not exploited or shared, and therefore it is forgotten in a relatively short time. The emerging technologies of visualization and the human perceptual system can be exploited to improve access to large information spaces facilitating the patterns detection. Moreover, the use of visual elements that contain representations of the real world that are known a priori by the target group and that are part of his world view, allows that the knowledge presented by these representations can be easily related to their prior knowledge, thereby facilitating learning. Despite visual representations have been used to support knowledge dissemination, no models have been proposed to integrate knowledge engineering methods and techniques with the use of images as a medium to retrieve and display knowledge. This work presents a model that aims to facilitate the visualization of the knowledge stored in digital repositories using semantic images. Through the semantic images, the user can retrieve and visualize the knowledge related to the entities represented in the image regions. The semantic images are visual representations of the real world which are known in advance by the target group and have mechanisms to identify domain concepts represented in each region. The proposed model is based on the framework for visualization of knowledge proposed by Burkhard and describes the interactions of users with the images. A prototype was eveloped to demonstrate the feasibility of the model using archetypes in the field of anatomy, using the Foundational Model of Anatomy and the Unifiled Medical Language System as knowledge domain and the database of the Scientific Electronic Library Online as a document repository

Synergistic Visualization And Quantitative Analysis Of Volumetric Medical Images

The medical diagnosis process starts with an interview with the patient, and continues with the physical exam. In practice, the medical professional may require additional screenings to precisely diagnose. Medical imaging is one of the most frequently used non-invasive screening methods to acquire insight of human body. Medical imaging is not only essential for accurate diagnosis, but also it can enable early prevention. Medical data visualization refers to projecting the medical data into a human understandable format at mediums such as 2D or head-mounted displays without causing any interpretation which may lead to clinical intervention. In contrast to the medical visualization, quantification refers to extracting the information in the medical scan to enable the clinicians to make fast and accurate decisions. Despite the extraordinary process both in medical visualization and quantitative radiology, efforts to improve these two complementary fields are often performed independently and synergistic combination is under-studied. Existing image-based software platforms mostly fail to be used in routine clinics due to lack of a unified strategy that guides clinicians both visually and quan- titatively. Hence, there is an urgent need for a bridge connecting the medical visualization and automatic quantification algorithms in the same software platform. In this thesis, we aim to fill this research gap by visualizing medical images interactively from anywhere, and performing a fast, accurate and fully-automatic quantification of the medical imaging data. To end this, we propose several innovative and novel methods. Specifically, we solve the following sub-problems of the ul- timate goal: (1) direct web-based out-of-core volume rendering, (2) robust, accurate, and efficient learning based algorithms to segment highly pathological medical data, (3) automatic landmark- ing for aiding diagnosis and surgical planning and (4) novel artificial intelligence algorithms to determine the sufficient and necessary data to derive large-scale problems

Faculty Publications and Creative Works 2002

Introduction One of the ways in which we recognize our faculty at the University of New Mexico is through Faculty Publications & Creative Works. An annual publication, it highlights our faculty\u27s scholarly and creative activities and achievements and serves as a compendium of UNM faculty efforts during the 2001 calendar year. Faculty Publications & Creative Works strives to illustrate the depth and breadth of research activities performed throughout our University\u27s laboratories, studios and classrooms. We believe that the communication of individual research is a significant method of sharing concepts and thoughts and ultimately inspiring the birth of new ideas. In support of this, UNM faculty during 2002 produced over 2,278 works, including 1,735 scholarly papers and articles, 64 books, 195 book chapters, 174 reviews, 84 creative works and 26 patented works. We are proud of the accomplishments of our faculty which are in part reflected in this book, which illustrates the diversity of intellectual pursuits in support of research and education at the University of New Mexico. Terry Yates Vice Provost for Researc