Overview of ALICE results on azimuthal correlations using neutral- and heavy-meson triggers

The ALICE detector is dedicated to studying the properties of hot and dense matter created in heavy-ion collisions. Among the probes used to investigate these properties are high-momentum particles, which originate in hard-scatterings occurring before the fireball creation. The fragments of hard scatterings interact with the hot and dense matter and via this interaction their spectra and azimuthal distributions are modified. This is probed by the measurement of the nuclear modification factor, where the $p_{\mathrm{T}}$ spectra obtained in Pb-Pb collisions are compared to a pp baseline. A strong suppression of charged hadrons as well as neutral- and heavy-flavor mesons was observed at $p_{\mathrm{T}} > 4$ GeV/$c$. Azimuthal correlations, using high-momentum ($p_{\mathrm{T}} > 4$ GeV/$c$) hadrons as triggers, can provide further insight into how the presence of the medium modifies the final kinematic distributions of the particles. Comparison with theoretical models can be used to test their predictions about the properties of the medium. We give an overview of ALICE azimuthal-correlation measurements of neutral- and heavy-flavor mesons with charged hadrons in pp collisions at $\sqrt{s}=7$ TeV and Pb-Pb collisions at $\sqrt{s_{\rm{NN}}}=2.76$ TeV. We also present a measurement of the $\pi^{0}$ correlation with jets in pp collisions at $\sqrt{s}=7$ TeV.Comment: Proceedings of '10th International Workshop on High-pT Physics at RHIC/LHC era' conference, 9-12 September 2014, 9 pages, 7 figure

Experimental treatment of Quark and Gluon Jets

The separate study of quark and gluon jets is vital for the interpretation of multiple variables behaviour observed in both high-energy hadron and heavy-ion collisions in the present and future experiments. We propose a set of jet-energy dependent cuts to be used to distinguish between quark and gluon jets experimentally based on a Monte-Carlo study of their properties. Further, we introduce the possibility to calibrate these cuts via gamma-jet and multi-jet events, which represent clean production channels for quark and gluon jets, respectively. The calibration can happen on real data and thus, reduces the dependence of the method performance on Monte-Carlo model predictions.Comment: 5 pages, 3 figures, presented at the 6th Intenational High-pT at LHC Workshop in Utrecht, 201

Functional Implications of DNA Methylation in Adipose Biology.

The twin epidemics of obesity and type 2 diabetes (T2D) are a serious health, social, and economic issue. The dysregulation of adipose tissue biology is central to the development of these two metabolic disorders, as adipose tissue plays a pivotal role in regulating whole-body metabolism and energy homeostasis (1). Accumulating evidence indicates that multiple aspects of adipose biology are regulated, in part, by epigenetic mechanisms. The precise and comprehensive understanding of the epigenetic control of adipose tissue biology is crucial to identifying novel therapeutic interventions that target epigenetic issues. Here, we review the recent findings on DNA methylation events and machinery in regulating the developmental processes and metabolic function of adipocytes. We highlight the following points: 1) DNA methylation is a key epigenetic regulator of adipose development and gene regulation, 2) emerging evidence suggests that DNA methylation is involved in the transgenerational passage of obesity and other metabolic disorders, 3) DNA methylation is involved in regulating the altered transcriptional landscape of dysfunctional adipose tissue, 4) genome-wide studies reveal specific DNA methylation events that associate with obesity and T2D, and 5) the enzymatic effectors of DNA methylation have physiological functions in adipose development and metabolic function

Expected Utility Maximization and Conditional Value-at-Risk Deviation-based Sharpe Ratio in Dynamic Stochastic Portfolio Optimization

In this paper we investigate the expected terminal utility maximization approach for a dynamic stochastic portfolio optimization problem. We solve it numerically by solving an evolutionary Hamilton-Jacobi-Bellman equation which is transformed by means of the Riccati transformation. We examine the dependence of the results on the shape of a chosen utility function in regard to the associated risk aversion level. We define the Conditional value-at-risk deviation ($CVaRD$) based Sharpe ratio for measuring risk-adjusted performance of a dynamic portfolio. We compute optimal strategies for a portfolio investment problem motivated by the German DAX 30 Index and we evaluate and analyze the dependence of the $CVaRD$-based Sharpe ratio on the utility function and the associated risk aversion level

Transformation Method for Solving Hamilton-Jacobi-Bellman Equation for Constrained Dynamic Stochastic Optimal Allocation Problem

In this paper we propose and analyze a method based on the Riccati transformation for solving the evolutionary Hamilton-Jacobi-Bellman equation arising from the stochastic dynamic optimal allocation problem. We show how the fully nonlinear Hamilton-Jacobi-Bellman equation can be transformed into a quasi-linear parabolic equation whose diffusion function is obtained as the value function of certain parametric convex optimization problem. Although the diffusion function need not be sufficiently smooth, we are able to prove existence, uniqueness and derive useful bounds of classical H\"older smooth solutions. We furthermore construct a fully implicit iterative numerical scheme based on finite volume approximation of the governing equation. A numerical solution is compared to a semi-explicit traveling wave solution by means of the convergence ratio of the method. We compute optimal strategies for a portfolio investment problem motivated by the German DAX 30 Index as an example of application of the method

Exploiting the adaptation dynamics to predict the distribution of beneficial fitness effects

Adaptation of asexual populations is driven by beneficial mutations and therefore the dynamics of this process, besides other factors, depend on the distribution of beneficial fitness effects. It is known that on uncorrelated fitness landscapes, this distribution can only be of three types: truncated, exponential and power law. We performed extensive stochastic simulations to study the adaptation dynamics on rugged fitness landscapes, and identified two quantities that can be used to distinguish the underlying distribution of beneficial fitness effects. The first quantity studied here is the fitness difference between successive mutations that spread in the population, which is found to decrease in the case of truncated distributions, remain nearly a constant for exponentially decaying distributions and increase when the fitness distribution decays as a power law. The second quantity of interest, namely, the rate of change of fitness with time also shows quantitatively different behaviour for different beneficial fitness distributions. The patterns displayed by the two aforementioned quantities are found to hold for both low and high mutation rates. We discuss how these patterns can be exploited to determine the distribution of beneficial fitness effects in microbial experiments.Comment: Communicated to PLOS ON