3,237 research outputs found
Learning midlevel image features for natural scene and texture classification
This paper deals with coding of natural scenes in order to extract semantic information. We present a new scheme to project natural scenes onto a basis in which each dimension encodes statistically independent information. Basis extraction is performed by independent component analysis (ICA) applied to image patches culled from natural scenes. The study of the resulting coding units (coding filters) extracted from well-chosen categories of images shows that they adapt and respond selectively to discriminant features in natural scenes. Given this basis, we define global and local image signatures relying on the maximal activity of filters on the input image. Locally, the construction of the signature takes into account the spatial distribution of the maximal responses within the image. We propose a criterion to reduce the size of the space of representation for faster computation. The proposed approach is tested in the context of texture classification (111 classes), as well as natural scenes classification (11 categories, 2037 images). Using a common protocol, the other commonly used descriptors have at most 47.7% accuracy on average while our method obtains performances of up to 63.8%. We show that this advantage does not depend on the size of the signature and demonstrate the efficiency of the proposed criterion to select ICA filters and reduce the dimensio
The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
The numerous recent breakthroughs in machine learning (ML) make imperative to
carefully ponder how the scientific community can benefit from a technology
that, although not necessarily new, is today living its golden age. This Grand
Challenge review paper is focused on the present and future role of machine
learning in space weather. The purpose is twofold. On one hand, we will discuss
previous works that use ML for space weather forecasting, focusing in
particular on the few areas that have seen most activity: the forecasting of
geomagnetic indices, of relativistic electrons at geosynchronous orbits, of
solar flares occurrence, of coronal mass ejection propagation time, and of
solar wind speed. On the other hand, this paper serves as a gentle introduction
to the field of machine learning tailored to the space weather community and as
a pointer to a number of open challenges that we believe the community should
undertake in the next decade. The recurring themes throughout the review are
the need to shift our forecasting paradigm to a probabilistic approach focused
on the reliable assessment of uncertainties, and the combination of
physics-based and machine learning approaches, known as gray-box.Comment: under revie
Reconstrução e classificação de sequências de ADN desconhecidas
The continuous advances in DNA sequencing technologies and techniques
in metagenomics require reliable reconstruction and accurate classification
methodologies for the diversity increase of the natural repository while contributing
to the organisms' description and organization. However, after
sequencing and de-novo assembly, one of the highest complex challenges
comes from the DNA sequences that do not match or resemble any biological
sequence from the literature. Three main reasons contribute to this
exception: the organism sequence presents high divergence according to the
known organisms from the literature, an irregularity has been created in the
reconstruction process, or a new organism has been sequenced. The inability
to efficiently classify these unknown sequences increases the sample
constitution's uncertainty and becomes a wasted opportunity to discover
new species since they are often discarded.
In this context, the main objective of this thesis is the development and
validation of a tool that provides an efficient computational solution to
solve these three challenges based on an ensemble of experts, namely
compression-based predictors, the distribution of sequence content, and
normalized sequence lengths. The method uses both DNA and amino acid
sequences and provides efficient classification beyond standard referential
comparisons. Unusually, it classifies DNA sequences without resorting directly
to the reference genomes but rather to features that the species biological
sequences share. Specifically, it only makes use of features extracted
individually from each genome without using sequence comparisons.
RFSC was then created as a machine learning classification pipeline that
relies on an ensemble of experts to provide efficient classification in metagenomic
contexts. This pipeline was tested in synthetic and real data, both
achieving precise and accurate results that, at the time of the development
of this thesis, have not been reported in the state-of-the-art. Specifically, it
has achieved an accuracy of approximately 97% in the domain/type classification.Os contínuos avanços em tecnologias de sequenciação de ADN e técnicas
em meta genómica requerem metodologias de reconstrução confiáveis e de
classificação precisas para o aumento da diversidade do repositório natural,
contribuindo, entretanto, para a descrição e organização dos organismos.
No entanto, após a sequenciação e a montagem de-novo, um dos desafios
mais complexos advém das sequências de ADN que não correspondem ou se
assemelham a qualquer sequencia biológica da literatura. São três as principais
razões que contribuem para essa exceção: uma irregularidade emergiu
no processo de reconstrução, a sequência do organismo é altamente dissimilar
dos organismos da literatura, ou um novo e diferente organismo foi
reconstruído. A incapacidade de classificar com eficiência essas sequências
desconhecidas aumenta a incerteza da constituição da amostra e desperdiça
a oportunidade de descobrir novas espécies, uma vez que muitas vezes são
descartadas.
Neste contexto, o principal objetivo desta tese é fornecer uma solução computacional
eficiente para resolver este desafio com base em um conjunto
de especialistas, nomeadamente preditores baseados em compressão, a distribuição de conteúdo de sequência e comprimentos de sequência normalizados.
O método usa sequências de ADN e de aminoácidos e fornece classificação eficiente além das comparações referenciais padrão. Excecionalmente,
ele classifica as sequências de ADN sem recorrer diretamente a genomas
de referência, mas sim às características que as sequências biológicas da
espécie compartilham. Especificamente, ele usa apenas recursos extraídos
individualmente de cada genoma sem usar comparações de sequência. Além
disso, o pipeline é totalmente automático e permite a reconstrução sem referência de genomas a partir de reads FASTQ com a garantia adicional de
armazenamento seguro de informações sensíveis.
O RFSC é então um pipeline de classificação de aprendizagem automática
que se baseia em um conjunto de especialistas para fornecer classificação
eficiente em contextos meta genómicos. Este pipeline foi aplicado em dados
sintéticos e reais, alcançando em ambos resultados precisos e exatos que,
no momento do desenvolvimento desta dissertação, não foram relatados na
literatura. Especificamente, esta ferramenta desenvolvida, alcançou uma
precisão de aproximadamente 97% na classificação de domínio/tipo.Mestrado em Engenharia de Computadores e Telemátic
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