10,834 research outputs found
Modulated phases and devil's staircases in a layered mean-field version of the ANNNI model
We investigate the phase diagram of a spin- Ising model on a cubic
lattice, with competing interactions between nearest and next-nearest neighbors
along an axial direction, and fully connected spins on the sites of each
perpendicular layer. The problem is formulated in terms of a set of
noninteracting Ising chains in a position-dependent field. At low temperatures,
as in the standard mean-feild version of the Axial-Next-Nearest-Neighbor Ising
(ANNNI) model, there are many distinct spatially commensurate phases that
spring from a multiphase point of infinitely degenerate ground states. As
temperature increases, we confirm the existence of a branching mechanism
associated with the onset of higher-order commensurate phases. We check that
the ferromagnetic phase undergoes a first-order transition to the modulated
phases. Depending on a parameter of competition, the wave number of the striped
patterns locks in rational values, giving rise to a devil's staircase. We
numerically calculate the Hausdorff dimension associated with these
fractal structures, and show that increases with temperature but seems
to reach a limiting value smaller than .Comment: 17 pages, 6 figure
A importância do contributo da educação em ciências na promoção de uma cultura de segurança
A sociedade em que vivemos, em constante mudança marcada pela ciência e pela tecnologia, apresenta novos riscos que colocam a segurança dos cidadãos em causa. Torna-se por isso imprescindÃvel promover uma educação para a segurança, pessoal e social, com vista à mudança de comportamentos e à promoção de uma cultura de prevenção e segurança. A educação em ciências constitui um veÃculo de excelência para a concretização dessa finalidade, na medida em que permite o desenvolvimento de competências, propiciadas pelo conhecimento cientÃfico, necessárias para a identificação e avaliação dos riscos existentes no dia-a-dia e consequente tomada de decisões informada. Fundamenta-se assim a importância da educação para a segurança através de aprendizagens em ciências e apresentam-se propostas didácticas para os primeiros anos de escolaridade, com vista à sua operacionalização
Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI
We propose a new method for breast cancer screening from DCE-MRI based on a
post-hoc approach that is trained using weakly annotated data (i.e., labels are
available only at the image level without any lesion delineation). Our proposed
post-hoc method automatically diagnosis the whole volume and, for positive
cases, it localizes the malignant lesions that led to such diagnosis.
Conversely, traditional approaches follow a pre-hoc approach that initially
localises suspicious areas that are subsequently classified to establish the
breast malignancy -- this approach is trained using strongly annotated data
(i.e., it needs a delineation and classification of all lesions in an image).
Another goal of this paper is to establish the advantages and disadvantages of
both approaches when applied to breast screening from DCE-MRI. Relying on
experiments on a breast DCE-MRI dataset that contains scans of 117 patients,
our results show that the post-hoc method is more accurate for diagnosing the
whole volume per patient, achieving an AUC of 0.91, while the pre-hoc method
achieves an AUC of 0.81. However, the performance for localising the malignant
lesions remains challenging for the post-hoc method due to the weakly labelled
dataset employed during training.Comment: Submitted to Medical Image Analysi
Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography
We propose new methods for the prediction of 5-year mortality in elderly
individuals using chest computed tomography (CT). The methods consist of a
classifier that performs this prediction using a set of features extracted from
the CT image and segmentation maps of multiple anatomic structures. We explore
two approaches: 1) a unified framework based on deep learning, where features
and classifier are automatically learned in a single optimisation process; and
2) a multi-stage framework based on the design and selection/extraction of
hand-crafted radiomics features, followed by the classifier learning process.
Experimental results, based on a dataset of 48 annotated chest CTs, show that
the deep learning model produces a mean 5-year mortality prediction accuracy of
68.5%, while radiomics produces a mean accuracy that varies between 56% to 66%
(depending on the feature selection/extraction method and classifier). The
successful development of the proposed models has the potential to make a
profound impact in preventive and personalised healthcare.Comment: 9 page
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