A New \u3ci\u3eFlexamia\u3c/i\u3e (Homoptera: Cicadellidae: Deltocephalinae) From Southern Michigan

A new species, Flexamia huroni, is described from a prairie fen in south- eastern Michigan. This leafhopper is closely related to the western F. serrata B & T, a specialist on mat muhly (Muhlenbergia richardsonis). Like its sister species, F. huroni was found only in close association with mat muhly, a grass listed as a threatened species in Michigan and Wisconsin. The regional rarity of mat muhly, its association with a globally imperiled plant commnity (prairie fen) and the absence of F. huroni from several fens known to contain this grass, make this new Flexamia a strong candidate for listing as endangered in Michigan

Field-guided proton acceleration at reconnecting X-points in flares

An explicitly energy-conserving full orbit code CUEBIT, developed originally to describe energetic particle effects in laboratory fusion experiments, has been applied to the problem of proton acceleration in solar flares. The model fields are obtained from solutions of the linearised MHD equations for reconnecting modes at an X-type neutral point, with the additional ingredient of a longitudinal magnetic field component. To accelerate protons to the highest observed energies on flare timescales, it is necessary to invoke anomalous resistivity in the MHD solution. It is shown that the addition of a longitudinal field component greatly increases the efficiency of ion acceleration, essentially because it greatly reduces the magnitude of drift motions away from the vicinity of the X-point, where the accelerating component of the electric field is largest. Using plasma parameters consistent with flare observations, we obtain proton distributions extending up to gamma-ray-emitting energies (>1MeV). In some cases the energy distributions exhibit a bump-on-tail in the MeV range. In general, the shape of the distribution is sensitive to the model parameters.Comment: 14 pages, 4 figures, accepted for publication in Solar Physic

Taxa of Idiocerus Lewis new to Canada (Rhynchota: Homoptera: Cicadellidae)

Six new species and one new subspecies of <i>Idiocerus</i> are described: <i>I. cabottii</i> from N.S., <i>I. canae</i> from Alberta, <i>I. glacialis</i> and <i>I. indistinctus</i> from B.C., and <i>I. albolinea</i>, <i>I. musteus arsiniatus</i> and <i>I. setaceus</i>, widespread east of the Cordilleran region. The identities of <i>I. duzeei</i> Provancher (N.S.-Ont.) and <i>I. interruptus</i> Gillette & Baker (N.S.-Colo.) are discussed, and these species are removed from synonymy

Nuclear Model of Binding alpha-particles

The model of binding alpha-particles in nuclei is suggested. It is shown good (with the accuracy of 1-2%) description of the experimental binding energies in light and medium nuclear systems. Our preliminary calculations show enhancement of the binding energy for super heavy nuclei with Z~120.Comment: 4 pages, 2 figures, Will be puplished in World Scientific as Procs. Int. Symposium on Exotic Nuclei, "EXON - 2004", July 5 - 12, 2004, Peterhof, Russi

Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model

Bayesian calibration of computer models tunes unknown input parameters by comparing outputs with observations. For model outputs that are distributed over space, this becomes computationally expensive because of the output size. To overcome this challenge, we employ a basis representation of the model outputs and observations: we match these decompositions to carry out the calibration efficiently. In the second step, we incorporate the non-stationary behaviour, in terms of spatial variations of both variance and correlations, in the calibration. We insert two integrated nested Laplace approximation-stochastic partial differential equation parameters into the calibration. A synthetic example and a climate model illustration highlight the benefits of our approach

Causal connectivity of evolved neural networks during behavior

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics

Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify, arbitrarily, neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.Comment: In submission to Frontiers in Neuromorphic Engineerin