Primordial black holes in non-Gaussian regimes

Primordial black holes (PBHs) can form in the early Universe from the collapse of rare, large density fluctuations. They have never been observed, but this fact is enough to constrain the amplitude of fluctuations on very small scales which cannot be otherwise probed. Because PBHs form only in very rare large fluctuations, the number of PBHs formed is extremely sensitive to changes in the shape of the tail of the fluctuation distribution - which depends on the amount of non-Gaussianity present. We first study how local non-Gaussianity of arbitrary size up to fifth order affects the abundance and constraints from PBHs, finding that they depend strongly on even small amounts of non-Gaussianity and the upper bound on the allowed amplitude of the power spectrum can vary by several orders of magnitude. The sign of the non-linearity parameters (f_{NL}, g_{NL}, etc) are particularly important. We also study the abundance and constraints from PBHs in the curvaton scenario, in which case the complete non-linear probability distribution is known, and find that truncating to any given order (i.e. to order f_{NL} or g_{NL}, etc) does not give accurate results

Evaluating Feature Extraction Methods for Biomedical Word Sense Disambiguation

Evaluating Feature Extraction Methods for Biomedical WSD Clint Cuffy, Sam Henry and Bridget McInnes, PhD Virginia Commonwealth University, Richmond, Virginia, USA Introduction. Biomedical text processing is currently a high active research area but ambiguity is still a barrier to the processing and understanding of these documents. Many word sense disambiguation (WSD) approaches represent instances of an ambiguous word as a distributional context vector. One problem with using these vectors is noise -- information that is overly general and does not contribute to the word’s representation. Feature extraction approaches attempt to compensate for sparsity and reduce noise by transforming the data from high-dimensional space to a space of fewer dimensions. Currently, word embeddings [1] have become an increasingly popular method to reduce the dimensionality of vector representations. In this work, we evaluate word embeddings in a knowledge-based word sense disambiguation method. Methods. Context requiring disambiguation consists of an instance of an ambiguous word, and multiple denotative senses. In our method, each word is replaced with its respective word embedding and either summed or averaged to form a single instance vector representation. This also is performed for each sense of an ambiguous word using the sense’s definition obtained from the Unified Medical Language System (UMLS). We calculate the cosine similarity between each sense and instance vectors, and assign the instance the sense with the highest value. Evaluation. We evaluate our method on three biomedical WSD datasets: NLM-WSD, MSH-WSD and Abbrev. The word embeddings were trained on the titles and abstracts from the 2016 Medline baseline. We compare using two word embedding models, Skip-gram and Continuous Bag of Words (CBOW), and vary the word vector representational lengths, from one-hundred to one-thousand, and compare differences in accuracy. Results. The overall outcome of this method demonstrates fairly high accuracy at disambiguating biomedical instance context from groups of denotative senses. The results showed the Skip-gram model obtained a higher disambiguation accuracy than CBOW but the increase was not significant for all of the datasets. Similarly, vector representations of differing lengths displayed minimal change in results, often differing by mere tenths in percentage. We also compared our results to current state-of-the-art knowledge-based WSD systems, including those that have used word embeddings, showing comparable or higher disambiguation accuracy. Conclusion. Although biomedical literature can be ambiguous, our knowledge-based feature extraction method using word embeddings demonstrates a high accuracy in disambiguating biomedical text while eliminating variations of associated noise. In the future, we plan to explore additional dimensionality reduction methods and training data. [1] T. Mikolov, I. Sutskever, K. Chen, G. Corrado and J. Dean, Distributed representations of words and phrases and their compositionality, Advances in neural information processing systems, pp. 3111-3119, 2013.https://scholarscompass.vcu.edu/uresposters/1278/thumbnail.jp

Shallow landsliding and catchment connectivity within the Houpoto Forest, New Zealand.

Active landslides and their contribution to catchment connectivity have been investigated within the Houpoto Forest, North Island, New Zealand. The aim was to quantify the proportion of buffered versus coupled landslides and explore how specific physical conditions influenced differences in landslide connectivity. Landsliding and land use changes between 2007 and 2010 were identified and mapped from aerial photography, and the preliminary analyses and interpretations of these data are presented here. The data indicate that forest harvesting made some slopes more susceptible to failure, and consequently many landslides were triggered during subsequent heavy rainfall events. Failures were particularly widespread during two high magnitude (> 200 mm/day) rainfall events, as recorded in 2010 imagery. Connectivity was analysed by quantifying the relative areal extents of coupled and buffered landslides identified in the different images. Approximately 10 % of the landslides were identified as being coupled to the local stream network, and thus directly contributing to the sediment budget. Following liberation of landslides during high-magnitude events, low-magnitude events are thought to be capable of transferring more of this sediment to the channel. Subsequent re-planting of the slopes appears to have helped recovery by increasing the thresholds for failure, thus reducing the number of landslides during subsequent high-magnitude rainfall events. Associated with this is a reduction in slope-channel connectivity. These preliminary results highlight how site specific preconditioning, preparatory and triggering factors contribute to landslide distribution and connectivity, in addition to how efficient re-afforestation improves the rate of slope recovery

Obstetrical Haemorrhagic Mortality in a Tertiary Hospital of the Limpopo Province, South Africa

Background: Obstetric haemorrhage is potentially fatal. Its frequency contributes to assessment of population health. So this study purpose was to assess the maternal deathsdue to obstetric haemorrhage in a tertiary hospital of the Limpopo Province, South Africa. Methods: Allmaternal deaths, which occurred at Pietersburg Hospitalfrom January 2011 to December 2015 were reviewed. The hospital death register was used to collate the list of maternal deaths.Maternalage, parity, referring facility, date of admission and death, ward where death occurred, and causes of deathwere collected from delivery registers and patient medical records.Results: There were 232 maternal deaths of which 48 (20.7%) were due to obstetrical haemorrhage. The mean age of the 48 women was 31.7±6.7 year range 15-48 years. Thirty one of 48 haemorrhagic deaths (65%) occurred within 24 hours of admission, 16 of 48 (33%) had a parity of 3 or more, 19 (40%) died in ICU and 12 (25%) in casualty. Forty three of 48 women (90%) were referred, of which 36 (84%) were from district hospitals. Post-partum haemorrhage accounted for 39 of 48 (81%) deaths followed by unspecifiedante-partum haemorrhage accounting for 4 (8%) and placenta abruption 2 of 48 (4%).Conclusion: This study demonstrated that a maternal death due to obstetric haemorrhage remains a major concern in Limpopo Province. Post-partum haemorrhage was the main cause of deaths