HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting

Purpose: Magnetic resonance fingerprinting (MRF) methods typically rely on dictio-nary matching to map the temporal MRF signals to quantitative tissue parameters. Such approaches suffer from inherent discretization errors, as well as high computational complexity as the dictionary size grows. To alleviate these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting approach, referred to as HYDRA. Methods: HYDRA involves two stages: a model-based signature restoration phase and a learning-based parameter restoration phase. Signal restoration is implemented using low-rank based de-aliasing techniques while parameter restoration is performed using a deep nonlocal residual convolutional neural network. The designed network is trained on synthesized MRF data simulated with the Bloch equations and fast imaging with steady state precession (FISP) sequences. In test mode, it takes a temporal MRF signal as input and produces the corresponding tissue parameters. Results: We validated our approach on both synthetic data and anatomical data generated from a healthy subject. The results demonstrate that, in contrast to conventional dictionary-matching based MRF techniques, our approach significantly improves inference speed by eliminating the time-consuming dictionary matching operation, and alleviates discretization errors by outputting continuous-valued parameters. We further avoid the need to store a large dictionary, thus reducing memory requirements. Conclusions: Our approach demonstrates advantages in terms of inference speed, accuracy and storage requirements over competing MRF method

"Now all I care about is my future" - supporting the shift: framework for the effective resettlement of young people leaving custody: a summary

This document has been produced as part of the Beyond Youth Custody (BYC) programme, funded under the Big Lottery Fund’s Youth in Focus initiative. BYC has been designed to challenge, advance and promote better thinking in policy and practice for the effective and sustainable resettlement of young people after custody. The programme has published research reports, policy briefings and practitioner guidance on a number of key issues in resettlement including diversity, young people with background trauma, girls and young women, and engaging young people; all resources are available for download at www.beyondyouthcustody.net. The new framework presented here – which draws on findings from across the programme – proposes, for the first time internationally, a ‘theory of change’ for the sustainable re-entry of young people. This reconceptualisation of resettlement enables a better understanding of why practices previously shown by research to improve recidivism rates are effective. Consequently, the framework provides a new focus for resettlement services’ aims and objectives, and may be particularly useful as a common language for the inter-agency working that we know is essential when supporting young people. The framework has been designed as a resource for policy makers, decision makers, academics studying youth justice and anyone working with young people leaving custody. A visual representation of the framework outlined in this document can be found on the centre pages. A full version of this report, which includes references and suggestions for further reading, can be found at: www.beyondyouthcustody.net/publications

HeMIS: Hetero-Modal Image Segmentation

We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the proposed approach learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations (such as taking the mean) are well defined. Points in that space, which are averaged over modalities available at inference time, can then be further processed to yield the desired segmentation. As such, any combinatorial subset of available modalities can be provided as input, without having to learn a combinatorial number of imputation models. Evaluated on two neurological MRI datasets (brain tumors and MS lesions), the approach yields state-of-the-art segmentation results when provided with all modalities; moreover, its performance degrades remarkably gracefully when modalities are removed, significantly more so than alternative mean-filling or other synthesis approaches.Comment: Accepted as an oral presentation at MICCAI 201

The role of the family in resettlement

This practitioner’s guide unpicks different interpretations of ‘family’. It explores the family’s unique position to fulfil key characteristics that research has shown are associated with effective resettlement support. It highlights recommendations and considerations that can be adopted into the practices of those working with young people and their families. As well as outlining various ways that families can help with personal and structural support, the guide also provides tips for successfully engaging with family members and sets out ways of overcoming the challenges that exist to unlocking this important resource

How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers

Polarization in American politics has been extensively documented and analyzed for decades, and the phenomenon became all the more apparent during the 2016 presidential election, where Trump and Clinton depicted two radically different pictures of America. Inspired by this gaping polarization and the extensive utilization of Twitter during the 2016 presidential campaign, in this paper we take the first step in measuring polarization in social media and we attempt to predict individuals' Twitter following behavior through analyzing ones' everyday tweets, profile images and posted pictures. As such, we treat polarization as a classification problem and study to what extent Trump followers and Clinton followers on Twitter can be distinguished, which in turn serves as a metric of polarization in general. We apply LSTM to processing tweet features and we extract visual features using the VGG neural network. Integrating these two sets of features boosts the overall performance. We are able to achieve an accuracy of 69%, suggesting that the high degree of polarization recorded in the literature has started to manifest itself in social media as well.Comment: 16 pages, SocInfo 2017, 9th International Conference on Social Informatic

Isolated Character Forms from Dated Syriac Manuscripts

This paper describes a set of hand-isolated character samples selected from securely dated manuscripts written in Syriac between 300 and 1300 C.E., which are being made available for research purposes. The collection can be used for a number of applications, including ground truth for character segmentation and form analysis for paleographical dating. Several applications based upon convolutional neural networks demonstrate the possibilities of the data set