Transversity from two pion interference fragmentation

We present calculation on the azimuthal spin asymmetries for pion pair production in semi-inclusive deep inelastic scattering (SIDIS) process at both HERMES and COMPASS kinematics, with transversely polarized proton, deuteron and neutron targets. We calculate the asymmetry by adopting a set of parametrization of the interference fragmentation functions and two different models for the transversity. We find that the result for the proton target is insensitive to the approaches of the transversity but more helpful to understand the interference fragmentation functions. However, for the neutron target, which can be obtained through using deuteron and {$^3$He} targets, we find different predictions for different approaches to the transversity. Thus probing the two pion interference fragmentation from the neutron can provide us more interesting information on the transversity.Comment: 15 latex pages, 6 figures, to appear in PR

Outlier Detection Using Nonconvex Penalized Regression

This paper studies the outlier detection problem from the point of view of penalized regressions. Our regression model adds one mean shift parameter for each of the $n$ data points. We then apply a regularization favoring a sparse vector of mean shift parameters. The usual $L_1$ penalty yields a convex criterion, but we find that it fails to deliver a robust estimator. The $L_1$ penalty corresponds to soft thresholding. We introduce a thresholding (denoted by $\Theta$) based iterative procedure for outlier detection ($\Theta$-IPOD). A version based on hard thresholding correctly identifies outliers on some hard test problems. We find that $\Theta$-IPOD is much faster than iteratively reweighted least squares for large data because each iteration costs at most $O(np)$ (and sometimes much less) avoiding an $O(np^2)$ least squares estimate. We describe the connection between $\Theta$-IPOD and $M$-estimators. Our proposed method has one tuning parameter with which to both identify outliers and estimate regression coefficients. A data-dependent choice can be made based on BIC. The tuned $\Theta$-IPOD shows outstanding performance in identifying outliers in various situations in comparison to other existing approaches. This methodology extends to high-dimensional modeling with $p\gg n$, if both the coefficient vector and the outlier pattern are sparse

Terminal Proterozoic cyanobacterial blooms and phosphogenesis documented by the Doushantuo granular phosphorites II: Microbial diversity and C isotopes

An unprecedented period of phosphogenesis, along with massive deposition of black shales, major perturbations in the global carbon cycle and the rise of atmospheric oxygen, occurred in the terminal Proterozoic in the aftermath of the Marinoan glaciation. Although causal links between these processes have been postulated, evidence remains challenging. Correlated in situ micro-analyses of granular phosphorites from the Ediacaran Doushantuo Formation in Yichang, South China, suggested that cyanobacteria and associated extracellular polymeric substances (EPS) might have promoted aggregated granule growth and subsequent phosphatization (She et al., 2013). Here, we present new paleontological data for the Doushantuo phosphorites from Yichang, which, combined with Raman microspectroscopy and carbon isotope data, further document links between the biology of cyanobacteria and phosphogenesis. Mapping of microfossils in thin section shows that most phosphatic granules contain microfossils that are dominated by colonies of Myxococcoides, along with several filamentous genera generally considered to represent cyanobacterial sheaths. In addition, the phosphorites and associated rocks have δ13Corg values in the range of −26.0 to −29.7‰ VPDB, consistent with photoautotrophic carbon fixation with the Rubisco enzyme. Close association of phosphorites with the Marinoan tillites in stratigraphic level supports a genetic link between deglaciation and phosphogenesis, at least for the Doushantuo occurrence. Our new data suggest that major cyanobacterial blooms probably took place in the terminal Proterozoic, which might have resulted in rapid scavenging of bioavailable phosphorus and massive accumulations of organic matter (OM). Within a redox-stratified intra-shelf basin, the OM-bound phosphorus could have liberated by microbial sulfate reduction and other anaerobic metabolisms and subsequently concentrated by Fe-redox pumping below the chemocline. Upwelling of the bottom waters or upward fluctuation of the chemocline might have brought P-enriched waters to the photic zone, where it was again scavenged by cyanobacteria through their EPS to be subsequently precipitated as francolite. The feedbacks between enhanced continental weathering, cyanobacterial blooms, carbon burial, and accelerated phosphorus cycle thus controlled the marine biogeochemical changes, which led to further oxygenation of the atmosphere and oceans, ultimately paving the way for the rise of metazoans

Exact solution of the one-dimensional ballistic aggregation

An exact expression for the mass distribution $\rho(M,t)$ of the ballistic aggregation model in one dimension is derived in the long time regime. It is shown that it obeys scaling $\rho(M,t)=t^{-4/3}F(M/t^{2/3})$ with a scaling function $F(z)\sim z^{-1/2}$ for $z\ll 1$ and $F(z)\sim \exp(-z^3/12)$ for $z\gg 1$. Relevance of these results to Burgers turbulence is discussed.Comment: 11 pages, 2 Postscript figure

Dispersive stabilization of the inverse cascade for the Kolmogorov flow

It is shown by perturbation techniques and numerical simulations that the inverse cascade of kink-antikink annihilations, characteristic of the Kolmogorov flow in the slightly supercritical Reynolds number regime, is halted by the dispersive action of Rossby waves in the beta-plane approximation. For beta tending to zero, the largest excited scale is proportional to the logarithm of one over beta and differs strongly from what is predicted by standard dimensional phenomenology which ignores depletion of nonlinearity.Comment: 4 pages, LATEX, 3 figures. v3: revised version with minor correction

On the dynamics of a self-gravitating medium with random and non-random initial conditions

The dynamics of a one-dimensional self-gravitating medium, with initial density almost uniform is studied. Numerical experiments are performed with ordered and with Gaussian random initial conditions. The phase space portraits are shown to be qualitatively similar to shock waves, in particular with initial conditions of Brownian type. The PDF of the mass distribution is investigated.Comment: Latex, figures in eps, 23 pages, 11 figures. Revised versio

Burgers velocity fields and dynamical transport processes

We explore a connection of the forced Burgers equation with the Schr\"{o}dinger (diffusive) interpolating dynamics in the presence of deterministic external forces. This entails an exploration of the consistency conditions that allow to interpret dispersion of passive contaminants in the Burgers flow as a Markovian diffusion process. In general, the usage of a continuity equation $\partial_t\rho =-\nabla (\vec{v}\rho)$, where $\vec{v}=\vec{v}(\vec{x},t)$ stands for the Burgers field and $\rho$ is the density of transported matter, is at variance with the explicit diffusion scenario. Under these circumstances, we give a complete characterisation of the diffusive matter transport that is governed by Burgers velocity fields. The result extends both to the approximate description of the transport driven by an incompressible fluid and to motions in an infinitely compressible medium.Comment: Latex fil

Spurious diffusion in particle simulations of the Kolmogorov flow

Particle simulations of the Kolmogorov flow are analyzed by the Landau-Lifshitz fluctuating hydrodynamics. It is shown that a spurious diffusion of the center of mass corrupts the statistical properties of the flow. The analytical expression for the corresponding diffusion coefficient is derived.Comment: 10 pages, no figure

Handling dropout probability estimation in convolution neural networks using meta-heuristics

Deep learning-based approaches have been paramount in recent years, mainly due to their outstanding results in several application domains, ranging from face and object recognition to handwritten digit identification. Convolutional Neural Networks (CNN) have attracted a considerable attention since they model the intrinsic and complex brain working mechanisms. However, one main shortcoming of such models concerns their overfitting problem, which prevents the network from predicting unseen data effectively. In this paper, we address this problem by means of properly selecting a regularization parameter known as Dropout in the context of CNNs using meta-heuristic-driven techniques. As far as we know, this is the first attempt to tackle this issue using this methodology. Additionally, we also take into account a default dropout parameter and a dropout-less CNN for comparison purposes. The results revealed that optimizing Dropout-based CNNs is worthwhile, mainly due to the easiness in finding suitable dropout probability values, without needing to set new parameters empirically