1,298 research outputs found
Dynamical Linked Cluster Expansions: A Novel Expansion Scheme for Point-Link-Point-Interactions
Dynamical linked cluster expansions are linked cluster expansions with
hopping parameter terms endowed with their own dynamics. This amounts to a
generalization from 2-point to point-link-point interactions. We develop an
associated graph theory with a generalized notion of connectivity and describe
an algorithmic generation of the new multiple-line graphs. We indicate physical
applications to spin glasses, partially annealed neural networks and SU(N)
gauge Higgs systems. In particular the new expansion technique provides the
possibility of avoiding the replica-trick in spin glasses. We consider
variational estimates for the SU(2) Higgs model of the electroweak phase
transition. The results for the transition line, obtained by dynamical linked
cluster expansions, agree quite well with corresponding high precision Monte
Carlo results.Comment: 41 pages, latex2e, 10 postscript figure
Structural validity of the MACI psychopathy and narcissism scales: Evidence of multidimensionality and implications for use in research and screening
This study investigated the psychometric properties and predictive validity of three self-report scales (the Psychopathy Content Scale, the Psychopathy-16 scale, and the Egotistic scale) derived from the Millon Adolescent Clinical Inventory (MACI) to screen for the presence of psychopathic and narcissistic personality characteristics. Exploratory and confirmatory factor analyses were performed in a sample of 173 clinic-referred adolescents (ages 12-17), results from which suggested that these scales are multidimensional in nature. The Psychopathy Content Scale was best captured by a two-factor structure, with personality-based items loading on one factor and antisocial/impulsive behaviors loading on the second. The most parsimonious solution for the Psychopathy-16 scale was a three-factor model, characterized by callous and egocentric features on the first two factors and antisocial behaviors on the third. The Egotistic scale of the MACI was best represented by three factors, depicting features of self-confidence, exhibitionistic tendencies, and social conceit, respectively. Regression analyses supported the multidimensionality of these scales by showing divergent patterns of association with violent and nonviolent outcomes among the factors that composed the scales
Study of resonance light scattering for remote optical probing
Enhanced scattering and fluorescence processes in the visible and UV were investigated which will enable improved remote measurements of gas properties. The theoretical relationship between scattering and fluorescence from an isolated molecule in the approach to resonance is examined through analysis of the time dependence of re-emitted light following excitation of pulsed incident light. Quantitative estimates are developed for the relative and absolute intensities of fluorescence and resonance scattering. New results are obtained for depolarization of scattering excited by light at wavelengths within a dissociative continuum. The experimental work was performed in two separate facilities. One of these utilizes argon and krypton lasers, single moded by a tilted etalon, and a 3/4 meter double monochromator. This facility was used to determine properties of the re-emission from NO2, I2 and O3 excited by visible light. The second facility involves a narrow-line dye laser, and a 3/4 meter single monochromator. The dye laser produces pulsed light with 5 nsec pulse duration and 0.005 nm spectral width
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Measuring Wellbeing in a Healthcare Setting: a Qualitative Study of Staff and Service User Perspectives
Stochastic learning in a neural network with adapting synapses
We consider a neural network with adapting synapses whose dynamics can be
analitically computed. The model is made of neurons and each of them is
connected to input neurons chosen at random in the network. The synapses
are -states variables which evolve in time according to Stochastic Learning
rules; a parallel stochastic dynamics is assumed for neurons. Since the network
maintains the same dynamics whether it is engaged in computation or in learning
new memories, a very low probability of synaptic transitions is assumed. In the
limit with large and finite, the correlations of neurons and
synapses can be neglected and the dynamics can be analitically calculated by
flow equations for the macroscopic parameters of the system.Comment: 25 pages, LaTeX fil
A recurrent neural network with ever changing synapses
A recurrent neural network with noisy input is studied analytically, on the
basis of a Discrete Time Master Equation. The latter is derived from a
biologically realizable learning rule for the weights of the connections. In a
numerical study it is found that the fixed points of the dynamics of the net
are time dependent, implying that the representation in the brain of a fixed
piece of information (e.g., a word to be recognized) is not fixed in time.Comment: 17 pages, LaTeX, 4 figure
Hierarchical Self-Programming in Recurrent Neural Networks
We study self-programming in recurrent neural networks where both neurons
(the `processors') and synaptic interactions (`the programme') evolve in time
simultaneously, according to specific coupled stochastic equations. The
interactions are divided into a hierarchy of groups with adiabatically
separated and monotonically increasing time-scales, representing sub-routines
of the system programme of decreasing volatility. We solve this model in
equilibrium, assuming ergodicity at every level, and find as our
replica-symmetric solution a formalism with a structure similar but not
identical to Parisi's -step replica symmetry breaking scheme. Apart from
differences in details of the equations (due to the fact that here
interactions, rather than spins, are grouped into clusters with different
time-scales), in the present model the block sizes of the emerging
ultrametric solution are not restricted to the interval , but are
independent control parameters, defined in terms of the noise strengths of the
various levels in the hierarchy, which can take any value in [0,\infty\ket.
This is shown to lead to extremely rich phase diagrams, with an abundance of
first-order transitions especially when the level of stochasticity in the
interaction dynamics is chosen to be low.Comment: 53 pages, 19 figures. Submitted to J. Phys.
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