9,320 research outputs found
Improving Usability of Interactive Graphics Specification and Implementation with Picking Views and Inverse Transformations
Specifying and programming graphical interactions are difficult tasks,
notably because designers have difficulties to express the dynamics of the
interaction. This paper shows how the MDPC architecture improves the usability
of the specification and the implementation of graphical interaction. The
architecture is based on the use of picking views and inverse transforms from
the graphics to the data. With three examples of graphical interaction, we show
how to express them with the architecture, how to implement them, and how this
improves programming usability. Moreover, we show that it enables implementing
graphical interaction without a scene graph. This kind of code prevents from
errors due to cache consistency management
PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation
High-performance computing has recently seen a surge of interest in
heterogeneous systems, with an emphasis on modern Graphics Processing Units
(GPUs). These devices offer tremendous potential for performance and efficiency
in important large-scale applications of computational science. However,
exploiting this potential can be challenging, as one must adapt to the
specialized and rapidly evolving computing environment currently exhibited by
GPUs. One way of addressing this challenge is to embrace better techniques and
develop tools tailored to their needs. This article presents one simple
technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL,
two open-source toolkits that support this technique.
In introducing PyCUDA and PyOpenCL, this article proposes the combination of
a dynamic, high-level scripting language with the massive performance of a GPU
as a compelling two-tiered computing platform, potentially offering significant
performance and productivity advantages over conventional single-tier, static
systems. The concept of RTCG is simple and easily implemented using existing,
robust infrastructure. Nonetheless it is powerful enough to support (and
encourage) the creation of custom application-specific tools by its users. The
premise of the paper is illustrated by a wide range of examples where the
technique has been applied with considerable success.Comment: Submitted to Parallel Computing, Elsevie
Modelling Reactive Multimedia: Design and Authoring
Multimedia document authoring is a multifaceted activity, and authoring tools tend to concentrate on a restricted set of the activities involved in the creation of a multimedia artifact. In particular, a distinction may be drawn between the design and the implementation of a multimedia artifact.
This paper presents a comparison of three different authoring paradigms, based on the common case study of a simple interactive animation. We present details of its implementation using the three different authoring tools, MCF, Fran and SMIL 2.0, and we discuss the conclusions that may be drawn from our comparison of the three approaches
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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Next generation software environments : principles, problems, and research directions
The past decade has seen a burgeoning of research and development in software environments. Conferences have been devoted to the topic of practical environments, journal papers produced, and commercial systems sold. Given all the activity, one might expect a great deal of consensus on issues, approaches, and techniques. This is not the case, however. Indeed, the term "environment" is still used in a variety of conflicting ways. Nevertheless substantial progress has been made and we are at least nearing consensus on many critical issues.The purpose of this paper is to characterize environments, describe several important principles that have emerged in the last decade or so, note current open problems, and describe some approaches to these problems, with particular emphasis on the activities of one large-scale research program, the Arcadia project. Consideration is also given to two related topics: empirical evaluation and technology transition. That is, how can environments and their constituents be evaluated, and how can new developments be moved effectively into the production sector
Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology
Machine learning is revolutionizing image-based diagnostics in pathology and
radiology. ML models have shown promising results in research settings, but
their lack of interoperability has been a major barrier for clinical
integration and evaluation. The DICOM a standard specifies Information Object
Definitions and Services for the representation and communication of digital
images and related information, including image-derived annotations and
analysis results. However, the complexity of the standard represents an
obstacle for its adoption in the ML community and creates a need for software
libraries and tools that simplify working with data sets in DICOM format. Here
we present the highdicom library, which provides a high-level application
programming interface for the Python programming language that abstracts
low-level details of the standard and enables encoding and decoding of
image-derived information in DICOM format in a few lines of Python code. The
highdicom library ties into the extensive Python ecosystem for image processing
and machine learning. Simultaneously, by simplifying creation and parsing of
DICOM-compliant files, highdicom achieves interoperability with the medical
imaging systems that hold the data used to train and run ML models, and
ultimately communicate and store model outputs for clinical use. We demonstrate
through experiments with slide microscopy and computed tomography imaging,
that, by bridging these two ecosystems, highdicom enables developers to train
and evaluate state-of-the-art ML models in pathology and radiology while
remaining compliant with the DICOM standard and interoperable with clinical
systems at all stages. To promote standardization of ML research and streamline
the ML model development and deployment process, we made the library available
free and open-source
Computational Abstraction of Films for Quantitave Analysis of Cinematography
Currently, film viewersâ options for getting objective information about films before watching them, are limited. Comparisons are even harder to find and often require extensive film knowledge both by the author and the reader. Such comparisons are inherently subjective, therefore they limit the possibilities for scalable and effective statistical analyses. Apart from trailers, information about films cannot reach viewers audibly or visibly, which seems absurd considering the very nature of film.
The thesis examines repeatable quantification methods for computationally abstracting films in order to extract informative data for visualizations and further statistical analy- ses. Theoretical background empowered by multidisciplinary approach and design processes are described. Visualizations of analyses are provided and evaluated for their accuracy and efficiency.
Throughout the thesis foundations for the future automated quantification player/plugin, are described aiming to facilitate further developments. Theoretical structures of the website which may act as a gateway that collects and provides data for statistical cinematic research are also discussed
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