14 research outputs found
A network transparent, retained mode multimedia processing framework for the Linux operating system environment
Die Arbeit präsentiert ein Multimedia-Framework für Linux, das im Unterschied zu früheren Arbeiten auf den Ideen "retained-mode processing" und "lazy evaluation" basiert: Statt Transformationen unmittelbar auszuführen, wird eine abstrakte Repräsentation aller Medienelemente aufgebaut. "renderer"-Treiber fungieren als Übersetzer, die diese Darstellung zur Laufzeit in konkrete Operationen umsetzen, wobei das Datenmodell zahlreiche Optimierungen zur Reduktion der Anzahl der Schritte oder der Minimierung von Kommunikation erlaubt. Dies erlaubt ein stark vereinfachtes Programmiermodell bei gleichzeitiger Effizienzsteigerung. "renderer"-Treiber können zur Ausführung von Transformationen den lokalen Prozessor verwenden, oder können die Operationen delegieren. In der Arbeit wird eine Erweiterung des X Window Systems um Mechanismen zur Medienverarbeitung vorgestellt, sowie ein "renderer"-Treiber, der diese zur Delegation der Verarbeitung nutzt
Compressão eficiente de sequências biológicas usando uma rede neuronal
Background: The increasing production of genomic data has led to
an intensified need for models that can cope efficiently with the lossless
compression of biosequences. Important applications include long-term
storage and compression-based data analysis. In the literature, only a
few recent articles propose the use of neural networks for biosequence
compression. However, they fall short when compared with specific
DNA compression tools, such as GeCo2. This limitation is due to the
absence of models specifically designed for DNA sequences. In this
work, we combine the power of neural networks with specific DNA and
amino acids models. For this purpose, we created GeCo3 and AC2, two
new biosequence compressors. Both use a neural network for mixing
the opinions of multiple specific models.
Findings: We benchmark GeCo3 as a reference-free DNA compressor
in five datasets, including a balanced and comprehensive dataset
of DNA sequences, the Y-chromosome and human mitogenome, two
compilations of archaeal and virus genomes, four whole genomes, and
two collections of FASTQ data of a human virome and ancient DNA.
GeCo3 achieves a solid improvement in compression over the previous
version (GeCo2) of 2:4%, 7:1%, 6:1%, 5:8%, and 6:0%, respectively.
As a reference-based DNA compressor, we benchmark GeCo3 in four
datasets constituted by the pairwise compression of the chromosomes
of the genomes of several primates. GeCo3 improves the compression in
12:4%, 11:7%, 10:8% and 10:1% over the state-of-the-art. The cost of
this compression improvement is some additional computational time
(1:7_ to 3:0_ slower than GeCo2). The RAM is constant, and the tool
scales efficiently, independently from the sequence size. Overall, these
values outperform the state-of-the-art. For AC2 the improvements and
costs over AC are similar, which allows the tool to also outperform the
state-of-the-art.
Conclusions: The GeCo3 and AC2 are biosequence compressors with
a neural network mixing approach, that provides additional gains over
top specific biocompressors. The proposed mixing method is portable,
requiring only the probabilities of the models as inputs, providing easy
adaptation to other data compressors or compression-based data analysis
tools. GeCo3 and AC2 are released under GPLv3 and are available
for free download at https://github.com/cobilab/geco3 and
https://github.com/cobilab/ac2.Contexto: O aumento da produção de dados genómicos levou a uma
maior necessidade de modelos que possam lidar de forma eficiente com
a compressão sem perdas de biosequências. Aplicações importantes
incluem armazenamento de longo prazo e análise de dados baseada em
compressão. Na literatura, apenas alguns artigos recentes propõem o
uso de uma rede neuronal para compressão de biosequências. No entanto,
os resultados ficam aquém quando comparados com ferramentas
de compressão de ADN específicas, como o GeCo2. Essa limitação
deve-se à ausência de modelos específicos para sequências de ADN.
Neste trabalho, combinamos o poder de uma rede neuronal com modelos
específicos de ADN e aminoácidos. Para isso, criámos o GeCo3 e
o AC2, dois novos compressores de biosequências. Ambos usam uma
rede neuronal para combinar as opiniões de vários modelos específicos.
Resultados: Comparamos o GeCo3 como um compressor de ADN
sem referência em cinco conjuntos de dados, incluindo um conjunto
de dados balanceado de sequências de ADN, o cromossoma Y e o mitogenoma
humano, duas compilações de genomas de arqueas e vírus,
quatro genomas inteiros e duas coleções de dados FASTQ de um viroma
humano e ADN antigo. O GeCo3 atinge uma melhoria sólida
na compressão em relação à versão anterior (GeCo2) de 2,4%, 7,1%,
6,1%, 5,8% e 6,0%, respectivamente. Como um compressor de ADN
baseado em referência, comparamos o GeCo3 em quatro conjuntos
de dados constituídos pela compressão aos pares dos cromossomas
dos genomas de vários primatas. O GeCo3 melhora a compressão em
12,4%, 11,7%, 10,8% e 10,1% em relação ao estado da arte. O custo
desta melhoria de compressão é algum tempo computacional adicional
(1,7 _ a 3,0 _ mais lento do que GeCo2). A RAM é constante e a
ferramenta escala de forma eficiente, independentemente do tamanho
da sequência. De forma geral, os rácios de compressão superam o estado
da arte. Para o AC2, as melhorias e custos em relação ao AC são
semelhantes, o que permite que a ferramenta também supere o estado
da arte.
Conclusões: O GeCo3 e o AC2 são compressores de sequências biológicas
com uma abordagem de mistura baseada numa rede neuronal,
que fornece ganhos adicionais em relação aos biocompressores específicos
de topo. O método de mistura proposto é portátil, exigindo apenas
as probabilidades dos modelos como entradas, proporcionando uma fácil
adaptação a outros compressores de dados ou ferramentas de análise
baseadas em compressão. O GeCo3 e o AC2 são distribuídos sob GPLv3
e estão disponíveis para download gratuito em https://github.com/
cobilab/geco3 e https://github.com/cobilab/ac2.Mestrado em Engenharia de Computadores e Telemátic
Distributed and Communication-Efficient Continuous Data Processing in Vehicular Cyber-Physical Systems
Processing the data produced by modern connected vehicles is of increasing interest for vehicle manufacturers to gain knowledge and develop novel functions and applications for the future of mobility.Connected vehicles form Vehicular Cyber-Physical Systems (VCPSs) that continuously sense increasingly large data volumes from high-bandwidth sensors such as LiDARs (an array of laser-based distance sensors that create a 3D map of the surroundings).The straightforward attempt of gathering all raw data from a VCPS to a central location for analysis often fails due to limits imposed by the infrastructure on the communication and storage capacities. In this Licentiate thesis, I present the results from my research that investigates techniques aiming at reducing the data volumes that need to be transmitted from vehicles through online compression and adaptive selection of participating vehicles. As explained in this work, the key to reducing the communication volume is in pushing parts of the necessary processing onto the vehicles\u27 on-board computers, thereby favorably leveraging the available distributed processing infrastructure in a VCPS.The findings highlight that existing analysis workflows can be sped up significantly while reducing their data volume footprint and incurring only modest accuracy decreases. At the same time, the adaptive selection of vehicles for analyses proves to provide a sufficiently large subset of vehicles that have compliant data for further analyses, while balancing the time needed for selection and the induced computational load
Some new developments in image compression
This study is divided into two parts. The first part involves an investigation of near-lossless compression of digitized images using the entropy-coded DPCM method with a large number of quantization levels. Through the investigation, a new scheme that combines both lossy and lossless DPCM methods into a common framework is developed. This new scheme uses known results on the design of predictors and quantizers that incorporate properties of human visual perception. In order to enhance the compression performance of the scheme, an adaptively generated source model with multiple contexts is employed for the coding of the quantized prediction errors, rather than a memoryless model as in the conventional DPCM method. Experiments show that the scheme can provide compression in the range from 4 to 11 with a peak SNR of about 50 dB for 8-bit medical images. Also, the use of multiple contexts is found to improve compression performance by about 25% to 35%;The second part of the study is devoted to the problem of lossy image compression using tree-structured vector quantization. As a result of the study, a new design method for codebook generation is developed together with four different implementation algorithms. In the new method, an unbalanced tree-structured vector codebook is designed in a greedy fashion under the constraint of rate-distortion trade-off which can then be used to implement a variable-rate compression system. From experiments, it is found that the new method can achieve a very good rate-distortion performance while being computationally efficient. Also, due to the tree-structure of the codebook, the new method is amenable to progressive transmission applications
Algorithmic statistics: forty years later
Algorithmic statistics has two different (and almost orthogonal) motivations.
From the philosophical point of view, it tries to formalize how the statistics
works and why some statistical models are better than others. After this notion
of a "good model" is introduced, a natural question arises: it is possible that
for some piece of data there is no good model? If yes, how often these bad
("non-stochastic") data appear "in real life"?
Another, more technical motivation comes from algorithmic information theory.
In this theory a notion of complexity of a finite object (=amount of
information in this object) is introduced; it assigns to every object some
number, called its algorithmic complexity (or Kolmogorov complexity).
Algorithmic statistic provides a more fine-grained classification: for each
finite object some curve is defined that characterizes its behavior. It turns
out that several different definitions give (approximately) the same curve.
In this survey we try to provide an exposition of the main results in the
field (including full proofs for the most important ones), as well as some
historical comments. We assume that the reader is familiar with the main
notions of algorithmic information (Kolmogorov complexity) theory.Comment: Missing proofs adde
The 1995 Science Information Management and Data Compression Workshop
This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on October 26-27, 1995, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival, and retrieval of large quantities of data in future Earth and space science missions. It consisted of fourteen presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The Workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center
Towards a High Quality Real-Time Graphics Pipeline
Modern graphics hardware pipelines create photorealistic images with high geometric complexity in real time. The quality is constantly improving and advanced techniques from feature film visual effects, such as high dynamic range images and support for higher-order surface primitives, have recently been adopted. Visual effect techniques have large computational costs and significant memory bandwidth usage. In this thesis, we identify three problem areas and propose new algorithms that increase the performance of a set of computer graphics techniques. Our main focus is on efficient algorithms for the real-time graphics pipeline, but parts of our research are equally applicable to offline rendering. Our first focus is texture compression, which is a technique to reduce the memory bandwidth usage. The core idea is to store images in small compressed blocks which are sent over the memory bus and are decompressed on-the-fly when accessed. We present compression algorithms for two types of texture formats. High dynamic range images capture environment lighting with luminance differences over a wide intensity range. Normal maps store perturbation vectors for local surface normals, and give the illusion of high geometric surface detail. Our compression formats are tailored to these texture types and have compression ratios of 6:1, high visual fidelity, and low-cost decompression logic. Our second focus is tessellation culling. Culling is a commonly used technique in computer graphics for removing work that does not contribute to the final image, such as completely hidden geometry. By discarding rendering primitives from further processing, substantial arithmetic computations and memory bandwidth can be saved. Modern graphics processing units include flexible tessellation stages, where rendering primitives are subdivided for increased geometric detail. Images with highly detailed models can be synthesized, but the incurred cost is significant. We have devised a simple remapping technique that allowsfor better tessellation distribution in screen space. Furthermore, we present programmable tessellation culling, where bounding volumes for displaced geometry are computed and used to conservatively test if a primitive can be discarded before tessellation. We introduce a general tessellation culling framework, and an optimized algorithm for rendering of displaced Bézier patches, which is expected to be a common use case for graphics hardware tessellation. Our third and final focus is forward-looking, and relates to efficient algorithms for stochastic rasterization, a rendering technique where camera effects such as depth of field and motion blur can be faithfully simulated. We extend a graphics pipeline with stochastic rasterization in spatio-temporal space and show that stochastic motion blur can be rendered with rather modest pipeline modifications. Furthermore, backface culling algorithms for motion blur and depth of field rendering are presented, which are directly applicable to stochastic rasterization. Hopefully, our work in this field brings us closer to high quality real-time stochastic rendering
Improved Encoding for Compressed Textures
For the past few decades, graphics hardware has supported mapping a two dimensional image, or texture, onto a three dimensional surface to add detail during rendering. The complexity of modern applications using interactive graphics hardware have created an explosion of the amount of data needed to represent these images. In order to alleviate the amount of memory required to store and transmit textures, graphics hardware manufacturers have introduced hardware decompression units into the texturing pipeline. Textures may now be stored as compressed in memory and decoded at run-time in order to access the pixel data. In order to encode images to be used with these hardware features, many compression algorithms are run offline as a preprocessing step, often times the most time-consuming step in the asset preparation pipeline. This research presents several techniques to quickly serve compressed texture data. With the goal of interactive compression rates while maintaining compression quality, three algorithms are presented in the class of endpoint compression formats. The first uses intensity dilation to estimate compression parameters for low-frequency signal-modulated compressed textures and offers up to a 3X improvement in compression speed. The second, FasTC, shows that by estimating the final compression parameters, partition-based formats can choose an approximate partitioning and offer orders of magnitude faster encoding speed. The third, SegTC, shows additional improvement over selecting a partitioning by using a global segmentation to find the boundaries between image features. This segmentation offers an additional 2X improvement over FasTC while maintaining similar compressed quality. Also presented is a case study in using texture compression to benefit two dimensional concave path rendering. Compressing pixel coverage textures used for compositing yields both an increase in rendering speed and a decrease in storage overhead. Additionally an algorithm is presented that uses a single layer of indirection to adaptively select the block size compressed for each texture, giving a 2X increase in compression ratio for textures of mixed detail. Finally, a texture storage representation that is decoded at runtime on the GPU is presented. The decoded texture is still compressed for graphics hardware but uses 2X fewer bytes for storage and network bandwidth.Doctor of Philosoph
Advances in Genomic Data Compression
The rapid growth in the number of individual whole genome sequences and metagenomic
datasets is generating an unprecedented volume of genomic data. This is partly due to the
continuous drop in the cost of sequencing as well as growth in the utility of sequencing for
research and clinical purposes. We are now reaching a point whereby the lion share of the
cost is shifting from the actual sequencing to processing and storing the resulting data.
With genomic datasets reaching the petabyte scale in hospitals and medium to large
research groups, it is clear that there is an urgent need to store the data more efficiently - not
only to reduce current costs, but also to make sequencing even more affordable to an even
larger set of use cases, thereby accelerating the pace of adoption of genomic data for a
widening range of research projects and clinical applications.
In Chapter 1 of this thesis, I lay the groundwork for a new approach to compressing genomic
data—one that is based on an extensible software platform, which I called Genozip. This
initial proof of concept allows compression of data in a widely used format, namely the
Variant Call Format, or VCF (Danecek et al. 2011) . In Chapter 2, I expand on the work of
Chapter 1, showing how the software architecture is designed to support the addition of
genomic file formats, compression methods, and codecs. Benchmarking results show that
Genozip generally performs better and faster than the leading tools for compression of
common genomic data formats such as VCF, SAM (Li et al. 2009) and FASTQ (Cock et al.
2010) .
In Chapter 3, I take a detour from compression, and demonstrate how potentially Genozip,
with its detailed internal data structures for genomic file processing, could be used for other
types of data manipulation. As an example, I introduce DVCF, or Dual-coordinate VCF—an
extension of the VCF format that allows representation of genetic variants concurrently in
two coordinate systems defined by two different reference genomes (Lan 2021) . It is
possible to use a DVCF file in a pipeline where each step of the pipeline accesses the data
in either of the coordinate systems. I also developed novel methods for lifting over data from
one coordinate system to another, and show the superiority of my methods compared to the
two leading tools in that space, namely GATK LiftoverVCF (McKenna et al. 2010) and
CrossMap (Zhao et al. 2014) . Overall, the Genozip software package is a high quality and versatile bioinformatic tool that is already adopted by dozens of research and clinical laboratories worldwide. Through
reduction of the cost of whole genome sequencing data processing and storage, Genozip is
likely to further encourage the use of genomics in research and clinical settings.Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 202
The 1994 Silver Anniversary of APOLLO 11: From the Moon to the Stars
This report summarizes the technology transfer, advanced studies, and research and technology efforts in progress at Marshall Space Flight Center (MSFC) in 1994