A late-time transition in the equation of state versus Lambda-CDM

We study a model of the dark energy which exhibits a rapid change in its equation of state w(z), such as occurs in vacuum metamorphosis. We compare the model predictions with CMB, large scale structure and supernova data and show that a late-time transition is marginally preferred over standard Lambda-CDM.Comment: 4 pages, 1 figure, to appear in the proceedings of XXXVIIth Rencontres de Moriond, "The Cosmological Model", March 200

Machine Learning Classification of SDSS Transient Survey Images

We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any transient science pipeline and is currently still done by humans, but future surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate fully machine-enabled solutions. Using features trained from eigenimage analysis (principal component analysis, PCA) of single-epoch g, r and i-difference images, we can reach a completeness (recall) of 96 per cent, while only incorrectly classifying at most 18 per cent of artefacts as real objects, corresponding to a precision (purity) of 84 per cent. In general, random forests performed best, followed by the k-nearest neighbour and the SkyNet artificial neural net algorithms, compared to other methods such as na\"ive Bayes and kernel support vector machine. Our results show that PCA-based machine learning can match human success levels and can naturally be extended by including multiple epochs of data, transient colours and host galaxy information which should allow for significant further improvements, especially at low signal-to-noise.Comment: 14 pages, 8 figures. In this version extremely minor adjustments to the paper were made - e.g. Figure 5 is now easier to view in greyscal

Risk management by structured derivative product companies

In the early 1990s, some U.S. securities firms and foreign banks began creating subsidiary vehicles--known as structured derivative product companies (DPCs)--whose special risk management approaches enabled them to obtain triple-A credit ratings with the least amount of capital. At first, market observers expected credit-sensitive customers to turn increasingly to these DPCs. However, the authors find that structured DPCs--despite their superior ratings--have failed to live up to their initial promise and have yet to gain a competitive edge as intermediaries in the derivatives markets.Derivative securities ; Risk

Nonparametric Transient Classification using Adaptive Wavelets

Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classifier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients. The second novelty is the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The ranked classifier is simple and quick to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant, hence they do not need the light curves to be aligned to extract features. Further, BAGIDIS is nonparametric so it can be used for blind searches for new objects. We demonstrate the effectiveness of our ranked wavelet classifier against the well-tested Supernova Photometric Classification Challenge dataset in which the challenge is to correctly classify light curves as Type Ia or non-Ia supernovae. We train our ranked probability classifier on the spectroscopically-confirmed subsample (which is not representative) and show that it gives good results for all supernova with observed light curve timespans greater than 100 days (roughly 55% of the dataset). For such data, we obtain a Ia efficiency of 80.5% and a purity of 82.4% yielding a highly competitive score of 0.49 whilst implementing a truly "model-blind" approach to supernova classification. Consequently this approach may be particularly suitable for the classification of astronomical transients in the era of large synoptic sky surveys.Comment: 14 pages, 8 figures. Published in MNRA

Resolving Structure in Human Brain Organization: Identifying Mesoscale Organization in Weighted Network Representations

Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.Comment: Comments welcom

Examination of the Relative Influence of Vegetation, Distance from Inflow, and Elevation on Sedimentation in a Coastal Californian Wetland

Wetlands and floodplains can act as areas of sediment deposition and storage. Therefore, they have the capability to improve downstream water quality and physical habitat. However, sedimentation rates may vary greatly within even a single wetland or floodplain. Much of the knowledge on wetland sedimentation rates is based on studies in controlled wetlands, where the setting and inflow may be carefully manipulated. While wetland systems receiving unregulated inflows are far more abundant, they are not as well studied. Determining which environmental factors drive deposition patterns may allow land managers to optimize sedimentation in managed wetlands. Additionally, quantified rates of sedimentation and land accretion have become important for managers considering the likelihood of habitat conversion, such as from freshwater wetlands to brackish or salt marsh, given climate change and subsequent sea level rise. We evaluated the influence of vegetation type and density, elevation, and proximity to the point of inflow on sedimentation in a natural Californian wetland receiving unregulated inflows through model comparison and evidence ratios based on Akaike information criterion weights. In addition to generating an interpolated surface generated from 59 artificial grass mat sediment traps, we conducted a mass-balance sediment budget to act as an independent check of the total sedimentation in the wetland basin. Sedimentation values over the eight month study period ranged from 254.0 to 2875.2 g/m2, with an average of 1054.6 g/m2.We found strong evidence that distance from the point of inflow was the driving factor in depositional patterns, with vegetation also potentially playing a role. However, some of these postulated influences may have been confounded with each other; vegetation type and density were determined to be moderately correlated with distance from the point of inflow (R = 0.273 and R = 0.325, respectively). This limited our ability to conclude if vegetation was a driving influence on observed sedimentation patterns

Restoring the sting to metric preheating

The relative growth of field and metric perturbations during preheating is sensitive to initial conditions set in the preceding inflationary phase. Recent work suggests this may protect super-Hubble metric perturbations from resonant amplification during preheating. We show that this possibility is fragile and sensitive to the specific form of the interactions between the inflaton and other fields. The suppression is naturally absent in two classes of preheating in which either (1) the vacua of the non-inflaton fields during inflation are deformed away from the origin, or (2) the effective masses of non-inflaton fields during inflation are small but during preheating are large. Unlike the simple toy model of a $g^2 \phi^2 \chi^2$ coupling, most realistic particle physics models contain these other features. Moreover, they generically lead to both adiabatic and isocurvature modes and non-Gaussian scars on super-Hubble scales. Large-scale coherent magnetic fields may also appear naturally.Comment: 6 pages, 3 ps figures, RevTex, revised discussion of backreaction and new figure. To appear Phys. Rev. D (Rapid Communication

Network constraints on learnability of probabilistic motor sequences

Human learners are adept at grasping the complex relationships underlying incoming sequential input. In the present work, we formalize complex relationships as graph structures derived from temporal associations in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties inherent to those graph structures. Participants performed a probabilistic motor sequence task in which the order of button presses was determined by the traversal of graphs with modular, lattice-like, or random organization. Graph nodes each represented a unique button press and edges represented a transition between button presses. Results indicate that learning, indexed here by participants' response times, was strongly mediated by the graph's meso-scale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node's number of connections (degree) and a node's role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.Comment: 29 pages, 4 figure