"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

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

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

Discovering granger-causal features from deep learning networks

© Springer Nature Switzerland AG 2018. In this research, we propose deep networks that discover Granger causes from multivariate temporal data generated in financial markets. We introduce a Deep Neural Network (DNN) and a Recurrent Neural Network (RNN) that discover Granger-causal features for bivariate regression on bivariate time series data distributions. These features are subsequently used to discover Granger-causal graphs for multivariate regression on multivariate time series data distributions. Our supervised feature learning process in proposed deep regression networks has favourable F-tests for feature selection and t-tests for model comparisons. The experiments, minimizing root mean squared errors in the regression analysis on real stock market data obtained from Yahoo Finance, demonstrate that our causal features significantly improve the existing deep learning regression models

On the equivalence of pairing correlations and intrinsic vortical currents in rotating nuclei

The present paper establishes a link between pairing correlations in rotating nuclei and collective vortical modes in the intrinsic frame. We show that the latter can be embodied by a simple S-type coupling a la Chandrasekhar between rotational and intrinsic vortical collective modes. This results from a comparison between the solutions of microscopic calculations within the HFB and the HF Routhian formalisms. The HF Routhian solutions are constrained to have the same Kelvin circulation expectation value as the HFB ones. It is shown in several mass regions, pairing regimes, and for various spin values that this procedure yields moments of inertia, angular velocities, and current distributions which are very similar within both formalisms. We finally present perspectives for further studies.Comment: 8 pages, 4 figures, submitted to Phys. Rev.

Using deep learning for ordinal classification of mobile marketing user conversion

In this paper, we explore Deep Multilayer Perceptrons (MLP) to perform an ordinal classification of mobile marketing conversion rate (CVR), allowing to measure the value of product sales when an user clicks an ad. As a case study, we consider big data provided by a global mobile marketing company. Several experiments were held, considering a rolling window validation, different datasets, learning methods and performance measures. Overall, competitive results were achieved by an online deep learning model, which is capable of producing real-time predictions.This article is a result of the project NORTE-01-0247-FEDER-017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by Funda¸c˜ao para a Ciˆencia e Tecnologia (FCT) within the Project Scope: UID/CEC/00319/201

Reconstruction of Hydraulic Data by Machine Learning

Numerical simulation models associated with hydraulic engineering take a wide array of data into account to produce predictions: rainfall contribution to the drainage basin (characterized by soil nature, infiltration capacity and moisture), current water height in the river, topography, nature and geometry of the river bed, etc. This data is tainted with uncertainties related to an imperfect knowledge of the field, measurement errors on the physical parameters calibrating the equations of physics, an approximation of the latter, etc. These uncertainties can lead the model to overestimate or underestimate the flow and height of the river. Moreover, complex assimilation models often require numerous evaluations of physical solvers to evaluate these uncertainties, limiting their use for some real-time operational applications. In this study, we explore the possibility of building a predictor for river height at an observation point based on drainage basin time series data. An array of data-driven techniques is assessed for this task, including statistical models, machine learning techniques and deep neural network approaches. These are assessed on several metrics, offering an overview of the possibilities related to hydraulic time-series. An important finding is that for the same hydraulic quantity, the best predictors vary depending on whether the data is produced using a physical model or real observations.Comment: Submitted to SimHydro 201

A novel taxonomic marker that discriminates between morphologically complex actinomycetes

In the era where large whole genome bacterial data sets are generated routinely, rapid and accurate molecular systematics is becoming increasingly important. However, 16S ribosomal RNA sequencing does not always offer sufficient resolution to discriminate between closely related genera. The SsgA-like proteins (SALPs) are developmental regulatory proteins in sporulating actinomycete, whereby SsgB actively recruits FtsZ during sporulation-specific cell division. Here we present a novel method to classify actinomycetes, based on the extraordinary way the SsgA and SsgB proteins are conserved. The almost complete conservation of the SsgB amino acid sequence between members of the same genus, and its high divergence even between closely related genera, provides high quality data for the classification of morphologically complex actinomycetes. Our analysis validates Kitasatospora as a sister genus to Streptomyces in the family Streptomycetaceae and suggests that Micromonospora, Salinispora and Verrucosispora may represent different clades of the same genus. It is also apparent that the amino-acid sequence of SsgA is an accurate determinant for the ability of streptomycetes to produce submerged spores, dividing the phylogenetic tree of streptomycetes into LSp (liquid culture sporulation) and NLSp (no liquid culture sporulation) branches. A new phylogenetic tree of industrially relevant actinomycetes is presented and compared to that based on 16S rRNA sequences

Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction

Dynamical weather and climate prediction models underpin many studies of the Earth system and hold the promise of being able to make robust projections of future climate change based on physical laws. However, simulations from these models still show many differences compared with observations. Machine learning has been applied to solve certain prediction problems with great success, and recently it's been proposed that this could replace the role of physically-derived dynamical weather and climate models to give better quality simulations. Here, instead, a framework using machine learning together with physically-derived models is tested, in which it is learnt how to correct the errors of the latter from timestep to timestep. This maintains the physical understanding built into the models, whilst allowing performance improvements, and also requires much simpler algorithms and less training data. This is tested in the context of simulating the chaotic Lorenz '96 system, and it is shown that the approach yields models that are stable and that give both improved skill in initialised predictions and better long-term climate statistics. Improvements in long-term statistics are smaller than for single time-step tendencies, however, indicating that it would be valuable to develop methods that target improvements on longer time scales. Future strategies for the development of this approach and possible applications to making progress on important scientific problems are discussed.Comment: 26p, 7 figures To be published in Journal of Advances in Modeling Earth System