Melting of troilite at high pressure in a diamond cell by laser heating

A system for measuring melting temperatures at high pressures is described. The sample is heated with radiation from a YAG laser. The beam is reflected downward through a microscope objective, through the upper diamond anvil, and focused onto the sample. Hense, intense heating is produced only at the sample and not within the diamond anvils. A vidicon system is used to observe the sample during heating. Incandescent light from the heated sample passes back through the objective lens into a grating spectrometer. The spectrum of the incandescent light is received by the photodiode array and stored in the multichannel analyzer. These data can then be transferred to floppy disk for analysis. A curve fitting program is used to compare the spectra with standard blackbody curves and to determine the temperature. Pressure is measured by the ruby fluorescence method. The system was used to study the melting behavior of natural troilite (FeS)

Network constraints on learnability of probabilistic motor sequences

Human learners are adept at grasping the complex relationships underlying incoming sequential input. In the present work, we formalize complex relationships as graph structures derived from temporal associations in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties inherent to those graph structures. Participants performed a probabilistic motor sequence task in which the order of button presses was determined by the traversal of graphs with modular, lattice-like, or random organization. Graph nodes each represented a unique button press and edges represented a transition between button presses. Results indicate that learning, indexed here by participants' response times, was strongly mediated by the graph's meso-scale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node's number of connections (degree) and a node's role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.Comment: 29 pages, 4 figure

Massless Metric Preheating

Can super-Hubble metric perturbations be amplified exponentially during preheating ? Yes. An analytical existence proof is provided by exploiting the conformal properties of massless inflationary models. The traditional conserved quantity \zeta is non-conserved in many regions of parameter space. We include backreaction through the homogeneous parts of the inflaton and preheating fields and discuss the role of initial conditions on the post-preheating power-spectrum. Maximum field variances are strongly underestimated if metric perturbations are ignored. We illustrate this in the case of strong self-interaction of the decay products. Without metric perturbations, preheating in this case is very inefficient. However, metric perturbations increase the maximum field variances and give alternative channels for the resonance to proceed. This implies that metric perturbations can have a large impact on calculations of relic abundances of particles produced during preheating.Comment: 8 pages, 4 colour figures. Version to appear in Phys. Rev. D. Contains substantial new analysis of the ranges of parameter space for which large changes to the inflation-produced power spectrum are expecte

A mechanistic model of connector hubs, modularity, and cognition

The human brain network is modular--comprised of communities of tightly interconnected nodes. This network contains local hubs, which have many connections within their own communities, and connector hubs, which have connections diversely distributed across communities. A mechanistic understanding of these hubs and how they support cognition has not been demonstrated. Here, we leveraged individual differences in hub connectivity and cognition. We show that a model of hub connectivity accurately predicts the cognitive performance of 476 individuals in four distinct tasks. Moreover, there is a general optimal network structure for cognitive performance--individuals with diversely connected hubs and consequent modular brain networks exhibit increased cognitive performance, regardless of the task. Critically, we find evidence consistent with a mechanistic model in which connector hubs tune the connectivity of their neighbors to be more modular while allowing for task appropriate information integration across communities, which increases global modularity and cognitive performance

A new twist to preheating

Metric perturbations typically strengthen field resonances during preheating. In contrast we present a model in which the super-Hubble field resonances are completely {\em suppressed} when metric perturbations are included. The model is the nonminimal Fakir-Unruh scenario which is exactly solvable in the long-wavelength limit when metric perturbations are included, but exhibits exponential growth of super-Hubble modes in their absence. This gravitationally enhanced integrability is exceptional, both for its rarity and for the power with which it illustrates the importance of including metric perturbations in consistent studies of preheating. We conjecture a no-go result - there exists no {\em single-field} model with growth of cosmologically-relevant metric perturbations during preheating.Comment: 6 pages, 3 figures, Version to appear in Physical Review

Gravitational waves in preheating

We study the evolution of gravitational waves through the preheating era that follows inflation. The oscillating inflaton drives parametric resonant growth of scalar field fluctuations, and although super-Hubble tensor modes are not strongly amplified, they do carry an imprint of preheating. This is clearly seen in the Weyl tensor, which provides a covariant description of gravitational waves.Comment: 8 pages, 8 figures, Revte