Effects of initial flow velocity fluctuation in event-by-event (3+1)D hydrodynamics

Hadron spectra and elliptic flow in high-energy heavy-ion collisions are studied within a (3+1)D ideal hydrodynamic model with fluctuating initial conditions given by the AMPT Monte Carlo model. Results from event-by-event simulations are compared with experimental data at both RHIC and LHC energies. Fluctuations in the initial energy density come from not only the number of coherent soft interactions of overlapping nucleons but also incoherent semi-hard parton scatterings in each binary nucleon collision. Mini-jets from semi-hard parton scatterings are assumed to be locally thermalized through a Gaussian smearing and give rise to non-vanishing initial local flow velocities. Fluctuations in the initial flow velocities lead to harder transverse momentum spectra of final hadrons due to non-vanishing initial radial flow velocities. Initial fluctuations in rapidity distributions lead to expanding hot spots in the longitudinal direction and are shown to cause a sizable reduction of final hadron elliptic flow at large transverse momenta.Comment: 17 pages in RevTex, 18 figures, final version published in PR

Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks

We investigate the relationship of resting-state fMRI functional connectivity estimated over long periods of time with time-varying functional connectivity estimated over shorter time intervals. We show that using Pearson's correlation to estimate functional connectivity implies that the range of fluctuations of functional connections over short time scales is subject to statistical constraints imposed by their connectivity strength over longer scales. We present a method for estimating time-varying functional connectivity that is designed to mitigate this issue and allows us to identify episodes where functional connections are unexpectedly strong or weak. We apply this method to data recorded from $N=80$ participants, and show that the number of unexpectedly strong/weak connections fluctuates over time, and that these variations coincide with intermittent periods of high and low modularity in time-varying functional connectivity. We also find that during periods of relative quiescence regions associated with default mode network tend to join communities with attentional, control, and primary sensory systems. In contrast, during periods where many connections are unexpectedly strong/weak, default mode regions dissociate and form distinct modules. Finally, we go on to show that, while all functional connections can at times manifest stronger (more positively correlated) or weaker (more negatively correlated) than expected, a small number of connections, mostly within the visual and somatomotor networks, do so a disproportional number of times. Our statistical approach allows the detection of functional connections that fluctuate more or less than expected based on their long-time averages and may be of use in future studies characterizing the spatio-temporal patterns of time-varying functional connectivityComment: 47 Pages, 8 Figures, 4 Supplementary Figure

A NLO analysis on fragility of dihadron tomography in high energy AAAA collisions

The dihadron spectra in high energy $AA$ collisions are studied within the NLO pQCD parton model with jet quenching taken into account. The high $p_T$ dihadron spectra are found to be contributed not only by jet pairs close and tangential to the surface of the dense matter but also by punching-through jets survived at the center while the single hadron high $p_T$ spectra are only dominated by surface emission. Consequently, the suppression factor of such high-$p_T$ hadron pairs is found to be more sensitive to the initial gluon density than the single hadron suppression factor.Comment: 4 pages, 4 figures, proceedings for the 19th international Conference on ultra-relativistic nucleus-nucleus collisions (QM2006), Shanghai, China, November 14-20, 200

Fluctuations between high- and low-modularity topology in time-resolved functional connectivity

Modularity is an important topological attribute for functional brain networks. Recent studies have reported that modularity of functional networks varies not only across individuals being related to demographics and cognitive performance, but also within individuals co-occurring with fluctuations in network properties of functional connectivity, estimated over short time intervals. However, characteristics of these time-resolved functional networks during periods of high and low modularity have remained largely unexplored. In this study we investigate spatiotemporal properties of time-resolved networks in the high and low modularity periods during rest, with a particular focus on their spatial connectivity patterns, temporal homogeneity and test-retest reliability. We show that spatial connectivity patterns of time-resolved networks in the high and low modularity periods are represented by increased and decreased dissociation of the default mode network module from task-positive network modules, respectively. We also find that the instances of time-resolved functional connectivity sampled from within the high (low) modularity period are relatively homogeneous (heterogeneous) over time, indicating that during the low modularity period the default mode network interacts with other networks in a variable manner. We confirmed that the occurrence of the high and low modularity periods varies across individuals with moderate inter-session test-retest reliability and that it is correlated with previously-reported individual differences in the modularity of functional connectivity estimated over longer timescales. Our findings illustrate how time-resolved functional networks are spatiotemporally organized during periods of high and low modularity, allowing one to trace individual differences in long-timescale modularity to the variable occurrence of network configurations at shorter timescales.Comment: Reorganized the paper; to appear in NeuroImage; arXiv abstract shortened to fit within character limit

Dihadron Tomography of High-Energy Nuclear Collisions in NLO pQCD

Back-to-back dihadron spectra in high-energy heavy-ion collisions are studied within the next-to-leading order (NLO) perturbative QCD parton model with jet quenching incorporated via modified jet fragmentation functions due to radiative parton energy loss in dense medium. The experimentally observed appearance of back-to-back dihadrons at high $p_T$ is found to originate mainly from jet pairs produced close and tangential to the surface of the dense matter. However, a substantial fraction of observed high $p_T$ dihadrons also comes from jets produced at the center of the medium after losing finite amount of energy. Consequently, the suppression factor of such high-$p_T$ hadron pairs is found to be more sensitive to the initial gluon density than the single hadron spectra that are dominated by surface emission. A simultaneous $\chi^2$-fit to both the single and dihadron spectra can be achieved within a narrow range of the energy loss parameters $\epsilon_0=1.6-2.1$ GeV/fm. Because of the flattening of the initial jet production spectra, high $p_T$ dihadrons at the LHC energy are found to be more robust as probes of the dense medium.Comment: 4 pages in revtex with 5 figures, final version in PRL The numerical tables of the NLO single and dihadron spectra used in this manuscript can be downloaded from ftp://www-nsdth.lbl.gov/pub/xnwang/dihadron

Generative models of the human connectome

The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.Comment: 38 pages, 5 figures + 19 supplemental figures, 1 tabl

Strangeness Enhancement in p+Ap+A and S+AS+A Interactions at SPS Energies

The systematics of strangeness enhancement is calculated using the HIJING and VENUS models and compared to recent data on $\,pp\,$, $\,pA\,$ and $\,AA\,$ collisions at CERN/SPS energies ($200A\,\, GeV\,$). The HIJING model is used to perform a {\em linear} extrapolation from $pp$ to $AA$. VENUS is used to estimate the effects of final state cascading and possible non-conventional production mechanisms. This comparison shows that the large enhancement of strangeness observed in $S+Au$ collisions, interpreted previously as possible evidence for quark-gluon plasma formation, has its origins in non-equilibrium dynamics of few nucleon systems. % Strangeness enhancement %is therefore traced back to the change in the production dynamics %from $pp$ to minimum bias $pS$ and central $SS$ collisions. A factor of two enhancement of $\Lambda^{0}$ at mid-rapidity is indicated by recent $pS$ data, where on the average {\em one} projectile nucleon interacts with only {\em two} target nucleons. There appears to be another factor of two enhancement in the light ion reaction $SS$ relative to $pS$, when on the average only two projectile nucleons interact with two target ones.Comment: 29 pages, 8 figures in uuencoded postscript fil