Automated analysis of radar imagery of Venus: handling lack of ground truth

Lack of verifiable ground truth is a common problem in remote sensing image analysis. For example, consider the synthetic aperture radar (SAR) image data of Venus obtained by the Magellan spacecraft. Planetary scientists are interested in automatically cataloging the locations of all the small volcanoes in this data set; however, the problem is very difficult and cannot be performed with perfect reliability even by human experts. Thus, training and evaluating the performance of an automatic algorithm on this data set must be handled carefully. We discuss the use of weighted free-response receiver-operating characteristics (wFROCs) for evaluating detection performance when the “ground truth” is subjective. In particular, we evaluate the relative detection performance of humans and automatic algorithms. Our experimental results indicate that proper assessment of the uncertainty in “ground truth” is essential in applications of this nature

Automating the Hunt for Volcanoes on Venus

Our long-term goal is to develop a trainable tool for locating patterns of interest in large image databases. Toward this goal we have developed a prototype system, based on classical filtering and statistical pattern recognition techniques, for automatically locating volcanoes in the Magellan SAR database of Venus. Training for the specific volcano-detection task is obtained by synthesizing feature templates (via normalization and principal components analysis) from a small number of examples provided by experts. Candidate regions identified by a focus of attention (FOA) algorithm are classified based on correlations with the feature templates. Preliminary tests show performance comparable to trained human observers

Serial Position Effects in Short-term Visual Memory: A SIMPLE Explanation?

A version of Sternberg’s (1966) short-term, visual memory recognition paradigm with pictures of unfamiliar faces as stimuli was used in three experiments to assess the applicability of the distinctiveness based SIMPLE model proposed by Brown, Neath & Chater (2002). Initial simulations indicated that the amount of recency predicted increased as the parameter measuring the psychological distinctiveness of the stimulus material (c) increased, and that the amount of primacy was dependent on the extent of proactive interference from previously presented stimuli. The data from experiment 1, which used memory lists of four and five faces varying in visual similarity confirmed the predicted, extended recency effect. However, changes in visual similarity were not found to produce changes in c. In Experiments 2 and 3, the conditions that influence the magnitude of c were explored. These revealed that both the familiarity of the stimulus class before testing, and changes in familiarity due to perceptual learning, influenced distinctiveness as indexed by the parameter c. Overall the empirical data from all three experiments were well-fit by SIMPLE

Design and in Vitro Evaluation of a New Nano-Microparticulate System for Enhanced Aqueous-Phase Solubility of Curcumin

Curcumin, a yellow polyphenol derived from the turmeric Curcuma longa, has been associated with a diverse therapeutic potential including anti-inflammatory, antioxidant, antiviral, and anticancer properties. However, the poor aqueous solubility and low bioavailability of curcumin have limited its potential when administrated orally. In this study, curcumin was encapsulated in a series of novel nano-microparticulate systems developed to improve its aqueous solubility and stability. The nano-microparticulate systems are based entirely on biocompatible, biodegradable, and edible polymers including chitosan, alginate, and carrageenan. The particles were synthesized via ionotropic gelation. Encapsulating the curcumin into the hydrogel nanoparticles yielded a homogenous curcumin dispersion in aqueous solution compared to the free form of curcumin. Also, the in vitro release profile showed up to 95% release of curcumin from the developed nano-microparticulate systems after 9 hours in PBS at pH 7.4 when freeze-dried particles were used.CONACYTCUPIAPharmac

A Quantitative Comparison of Opacities Calculated Using the Distorted- Wave and R\boldsymbol{R}-Matrix Methods

The present debate on the reliability of astrophysical opacities has reached a new climax with the recent measurements of Fe opacities on the Z-machine at the Sandia National Laboratory \citep{Bailey2015}. To understand the differences between theoretical results, on the one hand, and experiments on the other, as well as the differences among the various theoretical results, detailed comparisons are needed. Many ingredients are involved in the calculation of opacities; deconstructing the whole process and comparing the differences at each step are necessary to quantify their importance and impact on the final results. We present here such a comparison using the two main approaches to calculate the required atomic data, the $R$-Matrix and distorted-wave methods, as well as sets of configurations and coupling schemes to quantify the effects on the opacities for the $Fe\ XVII$ and $Ni\ XIV$ ions.Comment: 10 pages, 2 figure

The effect of the visual exercise environment on the response to psychological stress: a pilot study

Background: Performing physical activity whilst exposed to nature can improve health. However, there is little evidence of its impact on stress outcomes. The aim of this study was to examine the influence of the visual exercise environment on the response to a psychosocial stressor. Methods: Eighteen participants were randomised to one of three conditions: i. control; ii. Nature or; iii. Built condition. Participants exercised for 30min on a treadmill at 50% of their VO2max whilst viewing a video of either a natural or built environment or a blank screen. Following the exercise, participants completed the Trier Social Stress Test (TSST), a standardised laboratory stressor. Salivary samples were collected before, during and after the TSST to calculate cortisol reactivity and recovery. Results: One-way ANOVA revealed a significant effect of viewing condition on cortisol reactivity [F (2, 11) = 4.686, p = .034; n2p= .460]; with significantly lower reactivity in the built compared to the nature condition (p = .027, d=1.73). There was no effect of condition on cortisol recovery (P>0.05; n2p= .257). Conclusions: In the context of the adverse health impact of lower (i.e. blunted) cortisol responding, these findings could indicate a negative impact of the built environment on stress responses

Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios.Comment: This revised version fixes two small typos in the published versio

Rights or containment? The politics of Aboriginal cultural heritage in Victoria

Aboriginal cultural heritage protection, and the legislative regimes that underpin it, constitute important mechanisms for Aboriginal people to assert their rights and responsibilities. This is especially so in Victoria, where legislation vests wide-ranging powers and control of cultural heritage with Aboriginal communities. However, the politics of cultural heritage, including its institutionalisation as a scientific body of knowledge within the state, can also result in a powerful limiting of Aboriginal rights and responsibilities. This paper examines the politics of cultural heritage through a case study of a small forest in north-west Victoria. Here, a dispute about logging has pivoted around differing conceptualisations of Aboriginal cultural heritage values and their management. Cultural heritage, in this case, is both a powerful tool for the assertion of Aboriginal rights and interests, but simultaneously a set of boundaries within which the state operates to limit and manage the challenge those assertions pose. The paper will argue that Aboriginal cultural heritage is a politically contested and shifting domain structured around Aboriginal law and politics, Australian statute and the legacy of colonial history

Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models

We express the mean and variance terms in a double exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the model, and whether they enter linearly or flexibly. When the variance term is null we obtain a generalized additive model, which becomes a generalized linear model if the predictors enter the mean linearly. The model is estimated using Markov chain Monte Carlo simulation and the methodology is illustrated using real and simulated data sets.Comment: 8 graphs 35 page