Systematic study of finite-size effects in quantum Monte Carlo calculations of real metallic systems

We present a systematic and comprehensive study of finite-size effects in diffusion quantum Monte Carlo calculations of metals. Several previously introduced schemes for correcting finite-size errors are compared for accuracy and efficiency, and practical improvements are introduced. In particular, we test a simple but efficient method of finite-size correction based on an accurate combination of twist averaging and density functional theory. Our diffusion quantum Monte Carlo results for lithium and aluminum, as examples of metallic systems, demonstrate excellent agreement between all of the approaches considered

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

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

The antiferromagnetic cross-coupled spin ladder: quantum fidelity and tensor networks approach

We investigate the phase diagram of the cross-coupled Heisenberg spin ladder with antiferromagnetic couplings. For this model there have been conflicting results for the existence of the columnar dimer phase, which was predicted on the basis of weak coupling field theory renormalisation group arguments. The numerical work on this model has been based on various approaches, including exact diagonalization, series expansions and density-matrix renormalization group calculations. Using the recently developed tensor network states and ground-state fidelity approach for quantum spin ladders we find no evidence for the existence of the columnar dimer phase. We also provide an argument based on the symmetry of the Hamiltonian which suggests that the phase diagram for antiferromagnetic couplings consists of a single line separating the rung-singlet and Haldane phases.Comment: 5 pages, 4 figure

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

Environmental tobacco smoke exposure and health disparities: 8-year longitudinal findings from a large cohort of Thai adults

BACKGROUND: In rich countries, smokers, active or passive, often belong to disadvantaged groups. Less is known of tobacco patterns in the developing world. Hence, we seek out to investigate mental and physical health consequences of smoke exposure as well as tobacco-related inequality in transitional middle-income Thailand. METHODS: We studied a nationwide cohort of 87,151 middle-aged and older adults that we have been following for eight years (2005–2013) for emerging chronic diseases. Logistic regression was used to identify attributes associated with passive smoke exposure. Longitudinal associations between smoke exposure and wellbeing (SF-8) or psychological distress (Kessler 6) were investigated with multiple linear regression or multivariate logistic regression analysis. RESULTS: A high proportion of cohort members, especially females, were passive smokers at home and at public transport stations; males were more exposed at workplace and recreational places. We observed a social gradient with more passive smoking in poorer people. We also observed a dose response relationship linking graded smoke exposures (current, former, passive, non-exposed) to less wellbeing and more psychological distress (p-trend < 0.001). Female smokers in general had less wellbeing and more distress. CONCLUSION: Our findings add to current knowledge on the impact of active and passive smoking on health in a transitional economy. Promotion of smoking cessation programs both in public and at home could also potentially reduce adverse disparities in health and wellbeing in middle and lower income settings such as Thailand.This study was supported by the International Collaborative Research Grants Scheme with joint grants from the Wellcome Trust UK (GR071587MA) and the Australian National Health and Medical Research Council (268055), and as a global health grant from the NHMRC (585426)

Synergy and Group Size in Microbial Cooperation

Microbes produce many molecules that are important for their growth and development, and the consumption of these secretions by nonproducers has recently become an important paradigm in microbial social evolution. Though the production of these public goods molecules has been studied intensely, little is known of how the benefits accrued and costs incurred depend on the quantity of public good molecules produced. We focus here on the relationship between the shape of the benefit curve and cellular density with a model assuming three types of benefit functions: diminishing, accelerating, and sigmoidal (accelerating then diminishing). We classify the latter two as being synergistic and argue that sigmoidal curves are common in microbial systems. Synergistic benefit curves interact with group sizes to give very different expected evolutionary dynamics. In particular, we show that whether or not and to what extent microbes evolve to produce public goods depends strongly on group size. We show that synergy can create an “evolutionary trap” which can stymie the establishment and maintenance of cooperation. By allowing density dependent regulation of production (quorum sensing), we show how this trap may be avoided. We discuss the implications of our results for experimental design