477 research outputs found
Equality and Diversity in the Further Education Workforce: Report to the Scottish Further Education Funding Council
Comparing Deep Recurrent Networks Based on the MAE Random Sampling, a First Approach
Recurrent neural networks have demonstrated to be good at tackling prediction problems, however due to their high sensitivity to
hyper-parameter configuration, finding an appropriate network is a tough task. Automatic hyper-parameter optimization methods have emerged to find the most suitable configuration to a given problem, but these methods are not generally adopted because of their high computational cost. Therefore, in this study we extend the MAE random sampling, a low-cost method to compare single-hidden layer architectures, to multiple-hidden-layer ones. We validate empirically our proposal and show that it is possible to predict and compare the expected performance of an hyper-parameter configuration in a low-cost way.Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂa Tech.
This research was partially funded by Ministerio de EconomÄąĚa, Industria y Competitividad, Gobierno de EspaĂąa, and European Regional Development Fund grant numbers TIN2016-81766-REDT (http://cirti.es) and TIN2017-88213-R (http://6city.lcc.uma.es)
Enforcement of Financial Reporting Regulation: Insights From New Data
We collect new data on the enforcement of reporting regulation. Our data is novel in two ways: we substantially expand the scope and the time-series coverage of existing datasets. It includes 32 enforcement variables measured from 2005 to 2022 in 29 European countries. Based on these data, we document two stylized facts. First, in terms of the level of enforcement, we observe contrasting patterns for enforcement âon the bookâ and âin practiceâ. While the former appears to have increased over recent years, the latter exhibits the opposite trend. Second, we observe that the variation in enforcement has decreased significantly over time. While there is a downward trend for both enforcement âon the booksâ and enforcement in âin practiceâ, the decrease is much more pronounced for the latter. These patterns have potential implications for our understanding of the recent evolution of the European enforcement system, which could provide potential lessons for other jurisdictions
Learning Optimal Deep Projection of F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes
Several diseases of parkinsonian syndromes present similar symptoms at early
stage and no objective widely used diagnostic methods have been approved until
now. Positron emission tomography (PET) with F-FDG was shown to be able
to assess early neuronal dysfunction of synucleinopathies and tauopathies.
Tensor factorization (TF) based approaches have been applied to identify
characteristic metabolic patterns for differential diagnosis. However, these
conventional dimension-reduction strategies assume linear or multi-linear
relationships inside data, and are therefore insufficient to distinguish
nonlinear metabolic differences between various parkinsonian syndromes. In this
paper, we propose a Deep Projection Neural Network (DPNN) to identify
characteristic metabolic pattern for early differential diagnosis of
parkinsonian syndromes. We draw our inspiration from the existing TF methods.
The network consists of a (i) compression part: which uses a deep network to
learn optimal 2D projections of 3D scans, and a (ii) classification part: which
maps the 2D projections to labels. The compression part can be pre-trained
using surplus unlabelled datasets. Also, as the classification part operates on
these 2D projections, it can be trained end-to-end effectively with limited
labelled data, in contrast to 3D approaches. We show that DPNN is more
effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201
End stage renal disease patients have a skewed T cell receptor Vβ repertoire
BACKGROUND: End stage renal disease (ESRD) is associated with defective T-cell mediated immunity. A diverse T-cell receptor (TCR) Vβ repertoire is central to effective T-cell mediated immune responses to foreign antigens. In this study, the effect of ESRD on TCR Vβ repertoire was assessed. RESULTS: A higher proportion of ESRD patients (68.9 %) had a skewed TCR Vβ repertoire compared to age and cytomegalovirus (CMV) â IgG serostatus matched healthy individuals (31.4 %, Pâ<â0.001). Age, CMV serostatus and ESRD were independently associated with an increase in shifting of the TCR Vβ repertoire. More differentiated CD8(+) T cells were observed in young ESRD patients with a shifted TCR Vβ repertoire. CD31-expressing naive T cells and relative telomere length of T cells were not significantly related to TCR Vβ skewing. CONCLUSIONS: ESRD significantly skewed the TCR Vβ repertoire particularly in the elderly population, which may contribute to the uremia-associated defect in T-cell mediated immunity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12979-015-0055-7) contains supplementary material, which is available to authorized users
Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet
Catheter segmentation in 3D ultrasound is important for computer-assisted
cardiac intervention. However, a large amount of labeled images are required to
train a successful deep convolutional neural network (CNN) to segment the
catheter, which is expensive and time-consuming. In this paper, we propose a
novel catheter segmentation approach, which requests fewer annotations than the
supervised learning method, but nevertheless achieves better performance. Our
scheme considers a deep Q learning as the pre-localization step, which avoids
voxel-level annotation and which can efficiently localize the target catheter.
With the detected catheter, patch-based Dual-UNet is applied to segment the
catheter in 3D volumetric data. To train the Dual-UNet with limited labeled
images and leverage information of unlabeled images, we propose a novel
semi-supervised scheme, which exploits unlabeled images based on hybrid
constraints from predictions. Experiments show the proposed scheme achieves a
higher performance than state-of-the-art semi-supervised methods, while it
demonstrates that our method is able to learn from large-scale unlabeled
images.Comment: Accepted by MICCAI 202
Phage display as a tool to study human autoantibodies and autoantigens in systemic autoimmune disease. Selection of recombinant (auto)-antibodies specific for human autoantigens in rheumatic disease (RA, SLE, SSc) from human autoimmune-patient and immunized chicken derived phage display libraries
Learning to Segment Microscopy Images with Lazy Labels
The need for labour intensive pixel-wise annotation is a major limitation of
many fully supervised learning methods for segmenting bioimages that can
contain numerous object instances with thin separations. In this paper, we
introduce a deep convolutional neural network for microscopy image
segmentation. Annotation issues are circumvented by letting the network being
trainable on coarse labels combined with only a very small number of images
with pixel-wise annotations. We call this new labelling strategy `lazy' labels.
Image segmentation is stratified into three connected tasks: rough inner region
detection, object separation and pixel-wise segmentation. These tasks are
learned in an end-to-end multi-task learning framework. The method is
demonstrated on two microscopy datasets, where we show that the model gives
accurate segmentation results even if exact boundary labels are missing for a
majority of annotated data. It brings more flexibility and efficiency for
training deep neural networks that are data hungry and is applicable to
biomedical images with poor contrast at the object boundaries or with diverse
textures and repeated patterns
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