Radio observations of peculiar galaxies

With the 1000-foot radiotelescope of the Arecibo Ionospheric Observatory we have observed the positions of 69 peculiar galaxies classified by Arp (1966). Several radio sources were detected, but they could be chance coincidences. Radio maps of the radio sources are given.Asociación Argentina de Astronomí

Radio observations of peculiar galaxies

With the 1000-foot radiotelescope of the Arecibo Ionospheric Observatory we have observed the positions of 69 peculiar galaxies classified by Arp (1966). Several radio sources were detected, but they could be chance coincidences. Radio maps of the radio sources are given.Asociación Argentina de Astronomí

Radio observations of peculiar galaxies

With the 1000-foot radiotelescope of the Arecibo Ionospheric Observatory we have observed the positions of 69 peculiar galaxies classified by Arp (1966). Several radio sources were detected, but they could be chance coincidences. Radio maps of the radio sources are given.Asociación Argentina de Astronomí

Some Subdwarf Models

In this paper we present the results obtained from the numerical integration of the equations for the case of subdwarfs with masses comparable and somewhat larger than that of the Sun.Asociación Argentina de Astronomí

Test-time unsupervised domain adaptation

Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain). This motivates the active field of domain adaptation. While some approaches to the problem require labelled data from the target domain, others adopt an unsupervised approach to domain adaptation (UDA). Evaluating UDA methods consists of measuring the model’s ability to generalise to unseen data in the target domain. In this work, we argue that this is not as useful as adapting to the test set directly. We therefore propose an evaluation framework where we perform test-time UDA on each subject separately. We show that models adapted to a specific target subject from the target domain outperform a domain adaptation method which has seen more data of the target domain but not this specific target subject. This result supports the thesis that unsupervised domain adaptation should be used at test-time, even if only using a single target-domain subject

Some Subdwarf Models

In this paper we present the results obtained from the numerical integration of the equations for the case of subdwarfs with masses comparable and somewhat larger than that of the Sun.Asociación Argentina de Astronomí

The Role of MRI Physics in Brain Segmentation CNNs: Achieving Acquisition Invariance and Instructive Uncertainties

Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter dependent biases. It is important therefore to design algorithms that are not only robust to images of differing contrasts, but also be able to generalise well to unseen ones, with a quantifiable measure of uncertainty. In this paper we demonstrate the efficacy of a physics-informed, uncertainty-aware, segmentation network that employs augmentation-time MR simulations and homogeneous batch feature stratification to achieve acquisition invariance. We show that the proposed approach also accurately extrapolates to out-of-distribution sequence samples, providing well calibrated volumetric bounds on these. We demonstrate a significant improvement in terms of coefficients of variation, backed by uncertainty based volumetric validation

Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation

We propose a new deep learning method for tumour segmentation when dealing with missing imaging modalities. Instead of producing one network for each possible subset of observed modalities or using arithmetic operations to combine feature maps, our hetero-modal variational 3D encoder-decoder independently embeds all observed modalities into a shared latent representation. Missing data and tumour segmentation can be then generated from this embedding. In our scenario, the input is a random subset of modalities. We demonstrate that the optimisation problem can be seen as a mixture sampling. In addition to this, we introduce a new network architecture building upon both the 3D U-Net and the Multi-Modal Variational Auto-Encoder (MVAE). Finally, we evaluate our method on BraTS2018 using subsets of the imaging modalities as input. Our model outperforms the current state-of-the-art method for dealing with missing modalities and achieves similar performance to the subset-specific equivalent networks.Comment: Accepted at MICCAI 201