Inheritance of the Sex-Determining Factor in the Absence of a Complete Y Chromosome in 46,XX Human Males

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71879/1/j.1749-6632.1987.tb25088.x.pd

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

Nocardia kroppenstedtii sp. nov., a novel actinomycete isolated from a lung transplant patient with a pulmonary infection

An actinomycete, strain N1286T, isolated from a lung transplant patient with a pulmonary infection, was provisionally assigned to the genus Nocardia. The strain had chemotaxonomic and morphological properties typical of members of the genus Nocardia and formed a distinct phyletic line in the Nocardia 16S rRNA gene tree. It was most closely related to Nocardia farcinica DSM 43665T (99.8% gene similarity) but was distinguished from the latter by a low level of DNA:DNA relatedness. These strains were also distinguished by a broad range of phenotypic properties. On the basis of these data, it is proposed that isolate N1286T (=DSM 45810T = NCTC 13617T) should be classified as the type strain of a new Nocardia species for which the name Nocardia kroppenstedtii is proposed

"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

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie