Spectral effects of dehydration on phyllosilicates

Six phyllosilicates were progressively dehydrated under controlled conditions in an effort to study the spectral effects of their dehydration. The spectra obtained at each level of hydration provide information that may be used in future spectroscopic observations of the planets, as well as a data set which compliments the existing body of terrestrial soil knowledge

Topological Speed Limits to Network Synchronization

We study collective synchronization of pulse-coupled oscillators interacting on asymmetric random networks. We demonstrate that random matrix theory can be used to accurately predict the speed of synchronization in such networks in dependence on the dynamical and network parameters. Furthermore, we show that the speed of synchronization is limited by the network connectivity and stays finite, even if the coupling strength becomes infinite. In addition, our results indicate that synchrony is robust under structural perturbations of the network dynamics.Comment: 5 pages, 3 figure

Function-based Intersubject Alignment of Human Cortical Anatomy

Making conclusions about the functional neuroanatomical organization of the human brain requires methods for relating the functional anatomy of an individual's brain to population variability. We have developed a method for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by viewing a movie. Instead of basing alignment on functionally defined areas, whose location is defined as the center of mass or the local maximum response, the alignment is based on patterns of response as they are distributed spatially both within and across cortical areas. The method is implemented in the two-dimensional manifold of an inflated, spherical cortical surface. The method, although developed using movie data, generalizes successfully to data obtained with another cognitive activation paradigm—viewing static images of objects and faces—and improves group statistics in that experiment as measured by a standard general linear model (GLM) analysis

Heisenberg Hamiltonian description of multiple-sublattice itinerant-electron systems: General considerations and applications to NiMnSb and MnAs

We consider magnetic systems where the magnetic sublattices can be unambiguously separated into sublattices of inducing and induced moments. The concrete numerical calculations are performed for half-metallic ferromagnetic Heusler compound NiMnSb and hexagonal phase of MnAs. In both systems, Mn atoms possess a robust atomic moment that is much larger than the induced moments of other atoms. It is shown that the treatment of the induced moments as independent variables of the Heisenberg Hamiltonian leads to artificial features in the spin-wave spectrum. We show that the artificial features of the model do not have a dramatic influence on the estimated value of the Curie temperature. This is demonstrated within both mean-field approximation and random-phase approximation. It is shown that the calculational scheme where the induced moments are assumed to fully adjust their values and directions to the adiabatic magnetic configurations of the inducing moments is free from the artificial feature in the spin-wave spectra. In this scheme, the exchange interaction between the inducing and induced moments appears as renormalization of the exchange interactions between inducing moments. It is shown that the redistribution of the exchange interactions has strong influence on the estimated value of the Curie temperature because of the decreased number of the degrees of freedom in the thermodynamic model. Different schemes of the mapping of the systems on the Heisenberg Hamiltonian are examined. The similarities and differences in the properties of NiMnSb and MnAs are discussed

Storm‐time configuration of the inner magnetosphere: Lyon‐Fedder‐Mobarry MHD code, Tsyganenko model, and GOES observations

[1] We compare global magnetohydrodynamic (MHD) simulation results with an empirical model and observations to understand the magnetic field configuration and plasma distribution in the inner magnetosphere, especially during geomagnetic storms. The physics-based Lyon-Fedder-Mobarry (LFM) code simulates Earth\u27s magnetospheric topology and dynamics by solving the equations of ideal MHD. Quantitative comparisons of simulated events with observations reveal strengths and possible limitations and suggest ways to improve the LFM code. Here we present a case study that compares the LFM code to both a semiempirical magnetic field model and to geosynchronous measurements from GOES satellites. During a magnetic cloud event, the simulation and model predictions compare well qualitatively with observations, except during storm main phase. Quantitative statistical studies of the MHD simulation shows that MHD field lines are consistently under-stretched, especially during storm time (Dst \u3c −20 nT) on the nightside, a likely consequence of an insufficient representation of the inner magnetosphere current systems in ideal MHD. We discuss two approaches for improving the LFM result: increasing the simulation spatial resolution and coupling LFM with a ring current model based on drift physics (i.e., the Rice Convection Model (RCM)). We show that a higher spatial resolution LFM code better predicts geosynchronous magnetic fields (not only the average Bz component but also higher-frequency fluctuations driven by the solar wind). An early version of the LFM/RCM coupled code, which runs so far only for idealized events, yields a much-improved ring current, quantifiable by decreased field strengths at all local times compared to the LFM-only code

Learning to Order Things

There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order instances given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a two-stage approach in which one first learns by conventional means a binary preference function indicating whether it is advisable to rank one instance before another. Here we consider an on-line algorithm for learning preference functions that is based on Freund and Schapire's 'Hedge' algorithm. In the second stage, new instances are ordered so as to maximize agreement with the learned preference function. We show that the problem of finding the ordering that agrees best with a learned preference function is NP-complete. Nevertheless, we describe simple greedy algorithms that are guaranteed to find a good approximation. Finally, we show how metasearch can be formulated as an ordering problem, and present experimental results on learning a combination of 'search experts', each of which is a domain-specific query expansion strategy for a web search engine

Control theory for principled heap sizing

We propose a new, principled approach to adaptive heap sizing based on control theory. We review current state-of-the-art heap sizing mechanisms, as deployed in Jikes RVM and HotSpot. We then formulate heap sizing as a control problem, apply and tune a standard controller algorithm, and evaluate its performance on a set of well-known benchmarks. We find our controller adapts the heap size more responsively than existing mechanisms. This responsiveness allows tighter virtual machine memory footprints while preserving target application throughput, which is ideal for both embedded and utility computing domains. In short, we argue that formal, systematic approaches to memory management should be replacing ad-hoc heuristics as the discipline matures. Control-theoretic heap sizing is one such systematic approach

Response of Spiking Neurons to Correlated Inputs

The effect of a temporally correlated afferent current on the firing rate of a leaky integrate-and-fire (LIF) neuron is studied. This current is characterized in terms of rates, auto and cross-correlations, and correlation time scale $\tau_c$ of excitatory and inhibitory inputs. The output rate $\nu_{out}$ is calculated in the Fokker-Planck (FP) formalism in the limit of both small and large $\tau_c$ compared to the membrane time constant $\tau$ of the neuron. By simulations we check the analytical results, provide an interpolation valid for all $\tau_c$ and study the neuron's response to rapid changes in the correlation magnitude.Comment: 4 pages, 3 figure