Stochastic gravitational-wave background from spin loss of black holes

Although spinning black holes are shown to be stable in vacuum in general relativity, there exists exotic mechanisms that can convert the spin energy of black holes into gravitational waves. Such waves may be very weak in amplitude, since the spin-down could take a long time, and a direct search may not be feasible. We propose to search for the stochastic background associated with the spin-down, and we relate the level of this background to the formation rate of spinning black holes from the merger of binary black holes, as well as the energy spectrum of waves emitted by the spin-down process. We argue that current LIGO-Virgo observations are not inconsistent with the existence of a spin-down process, as long as it is slow enough. On the other hand, the background may still exist as long as a moderate fraction of spin energy is emitted within Hubble time. This stochastic background could be one interesting target of next generation GW detector network, such as LIGO Voyager, and could be extracted from total stochastic background

Image Aesthetics Assessment Using Composite Features from off-the-Shelf Deep Models

Deep convolutional neural networks have recently achieved great success on image aesthetics assessment task. In this paper, we propose an efficient method which takes the global, local and scene-aware information of images into consideration and exploits the composite features extracted from corresponding pretrained deep learning models to classify the derived features with support vector machine. Contrary to popular methods that require fine-tuning or training a new model from scratch, our training-free method directly takes the deep features generated by off-the-shelf models for image classification and scene recognition. Also, we analyzed the factors that could influence the performance from two aspects: the architecture of the deep neural network and the contribution of local and scene-aware information. It turns out that deep residual network could produce more aesthetics-aware image representation and composite features lead to the improvement of overall performance. Experiments on common large-scale aesthetics assessment benchmarks demonstrate that our method outperforms the state-of-the-art results in photo aesthetics assessment.Comment: Accepted by ICIP 201

Measuring education inequality - Gini coefficients of education

The authors use a Gini index to measure inequality in educational attainment. They present two methods (direct and indirect) for calculating an education Gini index, and generate a quinquennial data set on education Gini indexes for the over-15-population in 85 countries (1960-90). Preliminary empirical analysis suggests that: 1) Inequality in education in most of the countries declined over the three decades, with a few exceptions. 2) Inequality in education as measured by the education Gini index is negatively associated with average years of schooling, implying that countries with higher educational attainment are more likely to achieve equality in education, than those with lower attainment. 3) A clear pattern of an education Kuznets curve exists if the standard deviation of education is used. 4) Gender gaps are clearly related to education inequality, and over time, the association between gender gaps, and inequality becomes stronger. 5) Increases in per capita GDP (adjusted for purchasing power parity) seem to be negatively associated with education inequality, and positively related to labor force's average years of schooling, after controlling for initial income levels.Curriculum&Instruction,Teaching and Learning,Gender and Education,Education and Society,Primary Education