The nonlinear redshift-space power spectrum of galaxies

We study the power spectrum of galaxies in redshift space, with third order perturbation theory to include corrections that are absent in linear theory. We assume a local bias for the galaxies: i.e. the galaxy density is sampled from some local function of the underlying mass distribution. We find that the effect of the nonlinear bias in real space is to introduce two new features: first, there is a contribution to the power which is constant with wavenumber, whose nature we reveal as essentially a shot-noise term. In principle this contribution can mask the primordial power spectrum, and could limit the accuracy with which the latter might be measured on very large scales. Secondly, the effect of second- and third-order bias is to modify the effective bias (defined as the square root of the ratio of galaxy power spectrum to matter power spectrum). The effective bias is almost scale-independent over a wide range of scales. These general conclusions also hold in redshift space. In addition, we have investigated the distortion of the power spectrum by peculiar velocities, which may be used to constrain the density of the Universe. We look at the quadrupole-to-monopole ratio, and find that higher-order terms can mimic linear theory bias, but the bias implied is neither the linear bias, nor the effective bias referred to above. We test the theory with biased N-body simulations, and find excellent agreement in both real and redshift space, providing the local biasing is applied on a scale whose fractional r.m.s. density fluctuations are $< 0.5$.Comment: 13 pages, 7 figures. Accepted by MNRA

Exploring the impact of data poisoning attacks on machine learning model reliability

Recent years have seen the widespread adoption of Artificial Intelligence techniques in several domains, including healthcare, justice, assisted driving and Natural Language Processing (NLP) based applications (e.g., the Fake News detection). Those mentioned are just a few examples of some domains that are particularly critical and sensitive to the reliability of the adopted machine learning systems. Therefore, several Artificial Intelligence approaches were adopted as support to realize easy and reliable solutions aimed at improving the early diagnosis, personalized treatment, remote patient monitoring and better decision-making with a consequent reduction of healthcare costs. Recent studies have shown that these techniques are venerable to attacks by adversaries at phases of artificial intelligence. Poisoned data set are the most common attack to the reliability of Artificial Intelligence approaches. Noise, for example, can have a significant impact on the overall performance of a machine learning model. This study discusses the strength of impact of noise on classification algorithms. In detail, the reliability of several machine learning techniques to distinguish correctly pathological and healthy voices by analysing poisoning data was evaluated. Voice samples selected by available database, widely used in research sector, the Saarbruecken Voice Database, were processed and analysed to evaluate the resilience and classification accuracy of these techniques. All analyses are evaluated in terms of accuracy, specificity, sensitivity, F1-score and ROC area

The Bispectrum of IRAS Galaxies

We compute the bispectrum for the galaxy distribution in the IRAS QDOT, 2Jy, and 1.2Jy redshift catalogs for wavenumbers 0.05<k<0.2 h/Mpc and compare the results with predictions from gravitational instability in perturbation theory. Taking into account redshift space distortions, nonlinear evolution, the survey selection function, and discreteness and finite volume effects, all three catalogs show evidence for the dependence of the bispectrum on configuration shape predicted by gravitational instability. Assuming Gaussian initial conditions and local biasing parametrized by linear and non-linear bias parameters b_1 and b_2, a likelihood analysis yields 1/b_1 = 1.32^{+0.36}_{-0.58}, 1.15^{+0.39}_{-0.39} and b_2/b_1^2=-0.57^{+0.45}_{-0.30}, -0.50^{+0.31}_{-0.51}, for the for the 2Jy and 1.2Jy samples, respectively. This implies that IRAS galaxies trace dark matter increasingly weakly as the density contrast increases, consistent with their being under-represented in clusters. In a model with chi^2 non-Gaussian initial conditions, the bispectrum displays an amplitude and scale dependence different than that found in the Gaussian case; if IRAS galaxies do not have bias b_1> 1 at large scales, \chi^2 non-Gaussian initial conditions are ruled out at the 95% confidence level. The IRAS data do not distinguish between Lagrangian or Eulerian local bias.Comment: 30 pages, 11 figure

Hemoglobin is present as a canonical \u3b12\u3b22 tetramer in dopaminergic neurons

Hemoglobin is the oxygen carrier in blood erythrocytes. Oxygen coordination is mediated by \u3b12\u3b22 tetrameric structure via binding of the ligand to the heme iron atom. This structure is essential for hemoglobin function in the blood. In the last few years, expression of hemoglobin has been found in atypical sites, including the brain. Transcripts for \u3b1 and \u3b2 chains of hemoglobin as well as hemoglobin immunoreactivity have been shown in mesencephalic A9 dopaminergic neurons, whose selective degeneration leads to Parkinson's disease. To gain further insights into the roles of hemoglobin in the brain, we examined its quaternary structure in dopaminergic neurons in vitro and in vivo. Our results indicate that (i) in mouse dopaminergic cell line stably over-expressing \u3b1 and \u3b2 chains, hemoglobin exists as an \u3b12\u3b22 tetramer; (ii) similarly to the over-expressed protein, endogenous hemoglobin forms a tetramer of 64kDa; (iii) hemoglobin also forms high molecular weight insoluble aggregates; and (iv) endogenous hemoglobin retains its tetrameric structure in mouse mesencephalon in vivo. In conclusion, these results suggest that neuronal hemoglobin may be endowed with some of the biochemical activities and biological function associated to its role in erythroid cells. This article is part of a Special Issue entitled: Oxygen Binding and Sensing Proteins. \ua9 2013 The Authors. Published by Elsevier B.V. All rights reserved

VLCKD: a real time safety study in obesity

Background: Very Low-Calorie Ketogenic Diet (VLCKD) is currently a promising approach for the treatment of obesity. However, little is known about the side effects since most of the studies reporting them were carried out in normal weight subjects following Ketogenic Diet for other purposes than obesity. Thus, the aims of the study were: (1) to investigate the safety of VLCKD in subjects with obesity; (2) if VLCKD-related side effects could have an impact on its efficacy. Methods: In this prospective study we consecutively enrolled 106 subjects with obesity (12 males and 94 females, BMI 34.98 ± 5.43 kg/m2) that underwent to VLCKD. In all subjects we recorded side effects at the end of ketogenic phase and assessed anthropometric parameters at the baseline and at the end of ketogenic phase. In a subgroup of 25 subjects, we also assessed biochemical parameters. Results: No serious side effects occurred in our population and those that did occur were clinically mild and did not lead to discontinuation of the dietary protocol as they could be easily managed by healthcare professionals or often resolved spontaneously. Nine (8.5%) subjects stopped VLCKD before the end of the protocol for the following reasons: 2 (1.9%) due to palatability and 7 (6.1%) due to excessive costs. Finally, there were no differences in terms of weight loss percentage (13.5 ± 10.9% vs 18.2 ± 8.9%; p = 0.318) in subjects that developed side effects and subjects that did not developed side effects. Conclusion: Our study demonstrated that VLCKD is a promising, safe and effective therapeutic tool for people with obesity. Despite common misgivings, side effects are mild, transient and can be prevented and managed by adhering to the appropriate indications and contraindications for VLCKD, following well-organized and standardized protocols and performing adequate clinical and laboratory monitoring

The Power Spectrum, Bias Evolution, and the Spatial Three-Point Correlation Function

We calculate perturbatively the normalized spatial skewness, $S_3$, and full three-point correlation function (3PCF), $\zeta$, induced by gravitational instability of Gaussian primordial fluctuations for a biased tracer-mass distribution in flat and open cold-dark-matter (CDM) models. We take into account the dependence on the shape and evolution of the CDM power spectrum, and allow the bias to be nonlinear and/or evolving in time, using an extension of Fry's (1996) bias-evolution model. We derive a scale-dependent, leading-order correction to the standard perturbative expression for $S_3$ in the case of nonlinear biasing, as defined for the unsmoothed galaxy and dark-matter fields, and find that this correction becomes large when probing positive effective power-spectrum indices. This term implies that the inferred nonlinear-bias parameter, as usually defined in terms of the smoothed density fields, might depend on the chosen smoothing scale. In general, we find that the dependence of $S_3$ on the biasing scheme can substantially outweigh that on the adopted cosmology. We demonstrate that the normalized 3PCF, $Q$, is an ill-behaved quantity, and instead investigate $Q_V$, the variance-normalized 3PCF. The configuration dependence of $Q_V$ shows similarly strong sensitivities to the bias scheme as $S_3$, but also exhibits significant dependence on the form of the CDM power spectrum. Though the degeneracy of $S_3$ with respect to the cosmological parameters and constant linear- and nonlinear-bias parameters can be broken by the full configuration dependence of $Q_V$, neither statistic can distinguish well between evolving and non-evolving bias scenarios. We show that this can be resolved, in principle, by considering the redshift dependence of $\zeta$.Comment: 41 pages, including 12 Figures. To appear in The Astrophysical Journal, Vol. 521, #

Measuring the Nonlinear Biasing Function from a Galaxy Redshift Survey

We present a simple method for evaluating the nonlinear biasing function of galaxies from a redshift survey. The nonlinear biasing is characterized by the conditional mean of the galaxy density fluctuation given the underlying mass density fluctuation, or by the associated parameters of mean biasing and nonlinearity (following Dekel & Lahav 1999). Using the distribution of galaxies in cosmological simulations, at smoothing of a few Mpc, we find that the mean biasing can be recovered to a good accuracy from the cumulative distribution functions (CDFs) of galaxies and mass, despite the biasing scatter. Then, using a suite of simulations of different cosmological models, we demonstrate that the matter CDF is robust compared to the difference between it and the galaxy CDF, and can be approximated for our purpose by a cumulative log-normal distribution of 1+\delta with a single parameter \sigma. Finally, we show how the nonlinear biasing function can be obtained with adequate accuracy directly from the observed galaxy CDF in redshift space. Thus, the biasing function can be obtained from counts in cells once the rms mass fluctuation at the appropriate scale is assumed a priori. The relative biasing function between different galaxy types is measurable in a similar way. The main source of error is sparse sampling, which requires that the mean galaxy separation be smaller than the smoothing scale. Once applied to redshift surveys such as PSCz, 2dF, SDSS, or DEEP, the biasing function can provide valuable constraints on galaxy formation and structure evolution.Comment: 23 pages, 7 figures, revised version, accepted for publication in Ap

Statistical methods in cosmology

The advent of large data-set in cosmology has meant that in the past 10 or 20 years our knowledge and understanding of the Universe has changed not only quantitatively but also, and most importantly, qualitatively. Cosmologists rely on data where a host of useful information is enclosed, but is encoded in a non-trivial way. The challenges in extracting this information must be overcome to make the most of a large experimental effort. Even after having converged to a standard cosmological model (the LCDM model) we should keep in mind that this model is described by 10 or more physical parameters and if we want to study deviations from it, the number of parameters is even larger. Dealing with such a high dimensional parameter space and finding parameters constraints is a challenge on itself. Cosmologists want to be able to compare and combine different data sets both for testing for possible disagreements (which could indicate new physics) and for improving parameter determinations. Finally, cosmologists in many cases want to find out, before actually doing the experiment, how much one would be able to learn from it. For all these reasons, sophisiticated statistical techniques are being employed in cosmology, and it has become crucial to know some statistical background to understand recent literature in the field. I will introduce some statistical tools that any cosmologist should know about in order to be able to understand recently published results from the analysis of cosmological data sets. I will not present a complete and rigorous introduction to statistics as there are several good books which are reported in the references. The reader should refer to those.Comment: 31, pages, 6 figures, notes from 2nd Trans-Regio Winter school in Passo del Tonale. To appear in Lectures Notes in Physics, "Lectures on cosmology: Accelerated expansion of the universe" Feb 201