Squeeziness: An information theoretic measure for avoiding fault masking

Copyright @ 2012 ElsevierFault masking can reduce the effectiveness of a test suite. We propose an information theoretic measure, Squeeziness, as the theoretical basis for avoiding fault masking. We begin by explaining fault masking and the relationship between collisions and fault masking. We then define Squeeziness and demonstrate by experiment that there is a strong correlation between Squeeziness and the likelihood of collisions. We conclude with comments on how Squeeziness could be the foundation for generating test suites that minimise the likelihood of fault masking

Antiproton signatures from astrophysical and dark matter sources at the galactic center

The center of our Galaxy is a complex region characterized by extreme phenomena. The presence of the supermassive Sagittarius A* black hole, a high Dark Matter density and an even higher baryonic density are able to produce very energetic processes. Indeed, high energetic gamma rays have been observed by different telescopes, although its origin is not clear. In this work, we constrain the possible antiproton flux component associated to this signal. The expected secondary astrophysical antiproton background already saturates the observed data. It implies that any other important astrophysical source leads to an inconsistent excess, since the theoretical uncertainties corresponding to the mentioned background are small. The constraints depend on the diffusion model and the spectral features of the source. In particular, we consider antiproton spectra described by a power-law, a monochromatic signal and a Standard Model particle-antiparticle channel production.Comment: 16 pages, 12 figure

Beyond inverse Ising model: structure of the analytical solution for a class of inverse problems

I consider the problem of deriving couplings of a statistical model from measured correlations, a task which generalizes the well-known inverse Ising problem. After reminding that such problem can be mapped on the one of expressing the entropy of a system as a function of its corresponding observables, I show the conditions under which this can be done without resorting to iterative algorithms. I find that inverse problems are local (the inverse Fisher information is sparse) whenever the corresponding models have a factorized form, and the entropy can be split in a sum of small cluster contributions. I illustrate these ideas through two examples (the Ising model on a tree and the one-dimensional periodic chain with arbitrary order interaction) and support the results with numerical simulations. The extension of these methods to more general scenarios is finally discussed.Comment: 15 pages, 6 figure

A lower bound on the number of cosmic ray events required to measure source catalogue correlations

Recent analyses of cosmic ray arrival directions have resulted in evidence for a positive correlation with active galactic nuclei positions that has weak significance against an isotropic source distribution. In this paper, we explore the sample size needed to measure a highly statistically significant correlation to a parent source catalogue. We compare several scenarios for the directional scattering of ultra-high energy cosmic rays given our current knowledge of the galactic and intergalactic magnetic fields. We find significant correlations are possible for a sample of $>$1000 cosmic ray protons with energies above 60 EeV.Comment: 23 pages, 9 figure

Massless neutrino oscillations

Quantum dynamical semigroups provide a general framework for studying the evolution of open systems. Neutrino propagation both in vacuum and in matter can be analyzed using these techniques: they allow a consistent treatment of non-standard, dissipative effects that can alter the pattern of neutrino oscillations. In particular, initially massless neutrinos can give rise to a nonvanishing flavour transition probability, involving in addition the Majorana CP-violating mixing phase.Comment: 27 pages, plain-TeX, no figure

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Department of Computer Science and EngineeringAs deep learning has grown fast, so did the desire to interpret deep learning black boxes. As a result, many analysis tools have emerged to interpret it. Interpretation in deep learning has in fact popularized the use of deep learning in many areas including research, manufacturing, finance, and healthcare which needs relatively accurate and reliable decision making process. However, there is something we should not overlook. It is uncertainty. Uncertainties of models are directly reflected in the results of interpretations of model decision as explaining tools are dependent to models. Therefore, uncertainties of interpreting output from deep learning models should be also taken into account as quality and cost are directly impacted by measurement uncertainty. This attempt has not been made yet. Therefore, we suggest Bayesian input attribution rather than discrete input attribution by approximating Bayesian inference in deep Gaussian process through dropout to input attribution in this paper. Then we extract candidates that can sufficiently affect the output of the model, taking into account both input attribution itself and uncertainty of it.clos