DIAMONDS: a new Bayesian Nested Sampling tool. Application to Peak Bagging of solar-like oscillations

To exploit the full potential of Kepler light curves, sophisticated and robust analysis tools are now required more than ever. Characterizing single stars with an unprecedented level of accuracy and subsequently analyzing stellar populations in detail are fundamental to further constrain stellar structure and evolutionary models. We developed a new code, termed Diamonds, for Bayesian parameter estimation and model comparison by means of the nested sampling Monte Carlo (NSMC) algorithm, an efficient and powerful method very suitable for high-dimensional and multi-modal problems. A detailed description of the features implemented in the code is given with a focus on the novelties and differences with respect to other existing methods based on NSMC. Diamonds is then tested on the bright F8 V star KIC~9139163, a challenging target for peak-bagging analysis due to its large number of oscillation peaks observed, which are coupled to the blending that occurs between $\ell=2,0$ peaks, and the strong stellar background signal. We further strain the performance of the approach by adopting a 1147.5 days-long Kepler light curve. The Diamonds code is able to provide robust results for the peak-bagging analysis of KIC~9139163. We test the detection of different astrophysical backgrounds in the star and provide a criterion based on the Bayesian evidence for assessing the peak significance of the detected oscillations in detail. We present results for 59 individual oscillation frequencies, amplitudes and linewidths and provide a detailed comparison to the existing values in the literature. Lastly, we successfully demonstrate an innovative approach to peak bagging that exploits the capability of Diamonds to sample multi-modal distributions, which is of great potential for possible future automatization of the analysis technique.Comment: 22 pages, 14 figures, 3 tables. Accepted for publication in A&

Solar Magnetic Tracking. I. Software Comparison and Recommended Practices

Feature tracking and recognition are increasingly common tools for data analysis, but are typically implemented on an ad-hoc basis by individual research groups, limiting the usefulness of derived results when selection effects and algorithmic differences are not controlled. Specific results that are affected include the solar magnetic turnover time, the distributions of sizes, strengths, and lifetimes of magnetic features, and the physics of both small scale flux emergence and the small-scale dynamo. In this paper, we present the results of a detailed comparison between four tracking codes applied to a single set of data from SOHO/MDI, describe the interplay between desired tracking behavior and parameterization of tracking algorithms, and make recommendations for feature selection and tracking practice in future work.Comment: In press for Astrophys. J. 200

Using Granule to Search Privacy Preserving Voice in Home IoT Systems

The Home IoT Voice System (HIVS) such as Amazon Alexa or Apple Siri can provide voice-based interfaces for people to conduct the search tasks using their voice. However, how to protect privacy is a big challenge. This paper proposes a novel personalized search scheme of encrypting voice with privacy-preserving by the granule computing technique. Firstly, Mel-Frequency Cepstrum Coefficients (MFCC) are used to extract voice features. These features are obfuscated by obfuscation function to protect them from being disclosed the server. Secondly, a series of definitions are presented, including fuzzy granule, fuzzy granule vector, ciphertext granule, operators and metrics. Thirdly, the AES method is used to encrypt voices. A scheme of searchable encrypted voice is designed by creating the fuzzy granule of obfuscation features of voices and the ciphertext granule of the voice. The experiments are conducted on corpus including English, Chinese and Arabic. The results show the feasibility and good performance of the proposed scheme

Towards musical interaction : 'Schismatics' for e-violin and computer.

This paper discusses the evolution of the Max/MSP patch used in schismatics (2007, rev. 2010) for electric violin (Violectra) and computer, by composer Sam Hayden in collaboration with violinist Mieko Kanno. schismatics involves a standard performance paradigm of a fixed notated part for the e-violin with sonically unfixed live computer processing. Hayden was unsatisfied with the early version of the piece: the use of attack detection on the live e-violin playing to trigger stochastic processes led to an essentially reactive behaviour in the computer, resulting in a somewhat predictable one-toone sonic relationship between them. It demonstrated little internal relationship between the two beyond an initial e-violin ‘action’ causing a computer ‘event’. The revisions in 2010, enabled by an AHRC Practice-Led research award, aimed to achieve 1) a more interactive performance situation and 2) a subtler and more ‘musical’ relationship between live and processed sounds. This was realised through the introduction of sound analysis objects, in particular machine listening and learning techniques developed by Nick Collins. One aspect of the programming was the mapping of analysis data to synthesis parameters, enabling the computer transformations of the e-violin to be directly related to Kanno’s interpretation of the piece in performance