Dark matter annihilation and decay in dwarf spheroidal galaxies: The classical and ultrafaint dSphs

Dwarf spheroidal (dSph) galaxies are prime targets for present and future gamma-ray telescopes hunting for indirect signals of particle dark matter. The interpretation of the data requires careful assessment of their dark matter content in order to derive robust constraints on candidate relic particles. Here, we use an optimised spherical Jeans analysis to reconstruct the `astrophysical factor' for both annihilating and decaying dark matter in 21 known dSphs. Improvements with respect to previous works are: (i) the use of more flexible luminosity and anisotropy profiles to minimise biases, (ii) the use of weak priors tailored on extensive sets of contamination-free mock data to improve the confidence intervals, (iii) systematic cross-checks of binned and unbinned analyses on mock and real data, and (iv) the use of mock data including stellar contamination to test the impact on reconstructed signals. Our analysis provides updated values for the dark matter content of 8 `classical' and 13 `ultrafaint' dSphs, with the quoted uncertainties directly linked to the sample size; the more flexible parametrisation we use results in changes compared to previous calculations. This translates into our ranking of potentially-brightest and most robust targets---viz., Ursa Minor, Draco, Sculptor---, and of the more promising, but uncertain targets---viz., Ursa Major 2, Coma---for annihilating dark matter. Our analysis of Segue 1 is extremely sensitive to whether we include or exclude a few marginal member stars, making this target one of the most uncertain. Our analysis illustrates challenges that will need to be addressed when inferring the dark matter content of new `ultrafaint' satellites that are beginning to be discovered in southern sky surveys.Comment: 19 pages, 14 figures, submitted to MNRAS. Supplementary material available on reques

Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization

We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivated by total variation techniques, the method involves minimizing an energy functional made up of three terms. The first two terms promote a stepwise continuous classification function with sharp transitions between classes, while preserving symmetry among the class labels. The third term is a data fidelity term, allowing us to incorporate prior information into the model in a semi-supervised framework. The performance of the algorithm on synthetic data, as well as on the COIL and MNIST benchmark datasets, is competitive with state-of-the-art graph-based multiclass segmentation methods.Comment: 16 pages, to appear in Springer's Lecture Notes in Computer Science volume "Pattern Recognition Applications and Methods 2013", part of series on Advances in Intelligent and Soft Computin

Interference coloration as an anti-predator defence

Interference coloration, in which the perceived colour varies predictably with the angle of illumination or observation, is extremely widespread across animal groups. However, despite considerable advances in our understanding of the mechanistic basis of interference coloration in animals, we still have a poor understanding of its function. Here, I show, using avian predators hunting dynamic virtual prey, that the presence of interference coloration can significantly reduce a predator's attack success. Predators required more pecks to successfully catch interference-coloured prey compared with otherwise identical prey items that lacked interference coloration, and attacks against prey with interference colours were less accurate, suggesting that changes in colour or brightness caused by prey movement hindered a predator's ability to pinpoint their exact location. The pronounced antipredator benefits of interference coloration may explain why it has evolved independently so many times. © 2015 The Author(s) Published by the Royal Society. All rights reserved

Γ-stochastic neighbour embedding for feed-forward data visualization

t-distributed Stochastic Neighbour Embedding (t-SNE) is one of the most popular nonlinear dimension reduction techniques used in multiple application domains. In this paper we propose a variation on the embedding neighbourhood distribution, resulting in Γ-SNE, which can construct a feed-forward mapping using an RBF network. We compare the visualizations generated by Γ-SNE with those of t-SNE and provide empirical evidence suggesting the network is capable of robust interpolation and automatic weight regularization

Analysis of parallel process in HVAC systems using deep autoencoders

P. 15-26Heating, Ventilation, and Air Conditioning (HVAC) systems are generally built in a modular manner, comprising several identical subsystems in order to achieve their nominal capacity. These parallel subsystems and elements should have the same behavior and, therefore, differences between them can reveal failures and inefficiency in the system. The complexity in HVAC systems comes from the number of variables involved in these processes. For that reason, dimensionality reduction techniques can be a useful approach to reduce the complexity of the HVAC data and study their operation. However, for most of these techniques, it is not possible to project new data without retraining the projection and, as a result, it is not possible to easily compare several projections. In this paper, a method based on deep autoencoders is used to create a reference model with a HVAC system and new data is projected using this model to be able to compare them. The proposed approach is applied to real data from a chiller with 3 identical compressors at the Hospital of LeónS

Categorical Dimensions of Human Odor Descriptor Space Revealed by Non-Negative Matrix Factorization

In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain unclear. Here, we use non-negative matrix factorization (NMF) – a dimensionality reduction technique – to uncover structure in a panel of odor profiles, with each odor defined as a point in multi-dimensional descriptor space. The properties of NMF are favorable for the analysis of such lexical and perceptual data, and lead to a high-dimensional account of odor space. We further provide evidence that odor dimensions apply categorically. That is, odor space is not occupied homogenously, but rather in a discrete and intrinsically clustered manner. We discuss the potential implications of these results for the neural coding of odors, as well as for developing classifiers on larger datasets that may be useful for predicting perceptual qualities from chemical structures

Measuring the health impact of human rights violations related to Australian asylum policies and practices: A mixed methods study

This article has been made available through the Brunel Open Access Publishing Fund - Copyright @ 2009 Johnston et al.BACKGROUND: Human rights violations have adverse consequences for health. However, to date, there remains little empirical evidence documenting this association, beyond the obvious physical and psychological effects of torture. The primary aim of this study was to investigate whether Australian asylum policies and practices, which arguably violate human rights, are associated with adverse health outcomes. METHODS: We designed a mixed methods study to address the study aim. A cross-sectional survey was conducted with 71 Iraqi Temporary Protection Visa (TPV) refugees and 60 Iraqi Permanent Humanitarian Visa (PHV) refugees, residing in Melbourne, Australia. Prior to a recent policy amendment, TPV refugees were only given temporary residency status and had restricted access to a range of government funded benefits and services that permanent refugees are automatically entitled to. The quantitative results were triangulated with semi-structured interviews with TPV refugees and service providers. The main outcome measures were self-reported physical and psychological health. Standardised self-report instruments, validated in an Arabic population, were used to measure health and wellbeing outcomes. RESULTS: Forty-six percent of TPV refugees compared with 25% of PHV refugees reported symptoms consistent with a diagnosis of clinical depression (p = 0.003). After controlling for the effects of age, gender and marital status, TPV status made a statistically significant contribution to psychological distress (B = 0.5, 95% CI 0.3 to 0.71, p </= 0.001) amongst Iraqi refugees. Qualitative data revealed that TPV refugees generally felt socially isolated and lacking in control over their life circumstances, because of their experiences in detention and on a temporary visa. This sense of powerlessness and, for some, an implicit awareness they were being denied basic human rights, culminated in a strong sense of injustice. CONCLUSION: Government asylum policies and practices violating human rights norms are associated with demonstrable psychological health impacts. This link between policy, rights violations and health outcomes offers a framework for addressing the impact of socio-political structures on health.This research was supported by an Australian National and Medical Research Council PhD Scholarship (N. 251782) and a Victorian Health Promotion Foundation research grant (No. 2002-0280)

Model counting for complex data structures

We extend recent approaches for calculating the probability of program behaviors, to allow model counting for complex data structures with numeric fields. We use symbolic execution with lazy initialization to compute the input structures leading to the occurrence of a target event, while keeping a symbolic representation of the constraints on the numeric data. Off-the-shelf model counting tools are used to count the solutions for numerical constraints and field bounds encoding data structure invariants are used to reduce the search space. The technique is implemented in the Symbolic PathFinder tool and evaluated on several complex data structures. Results show that the technique is much faster than an enumeration-based method that uses the Korat tool and also highlight the benefits of using the field bounds to speed up the analysis

Sex-biased parental care and sexual size dimorphism in a provisioning arthropod

The diverse selection pressures driving the evolution of sexual size dimorphism (SSD) have long been debated. While the balance between fecundity selection and sexual selection has received much attention, explanations based on sex-specific ecology have proven harder to test. In ectotherms, females are typically larger than males, and this is frequently thought to be because size constrains female fecundity more than it constrains male mating success. However, SSD could additionally reflect maternal care strategies. Under this hypothesis, females are relatively larger where reproduction requires greater maximum maternal effort – for example where mothers transport heavy provisions to nests. To test this hypothesis we focussed on digger wasps (Hymenoptera: Ammophilini), a relatively homogeneous group in which only females provision offspring. In some species, a single large prey item, up to 10 times the mother’s weight, must be carried to each burrow on foot; other species provide many small prey, each flown individually to the nest. We found more pronounced female-biased SSD in species where females carry single, heavy prey. More generally, SSD was negatively correlated with numbers of prey provided per offspring. Females provisioning multiple small items had longer wings and thoraxes, probably because smaller prey are carried in flight. Despite much theorising, few empirical studies have tested how sex-biased parental care can affect SSD. Our study reveals that such costs can be associated with the evolution of dimorphism, and this should be investigated in other clades where parental care costs differ between sexes and species