On the evaluation of the specific heat and general off-diagonal n-point correlation functions within the loop algorithm

We present an efficient way to compute diagonal and off-diagonal n-point correlation functions for quantum spin-systems within the loop algorithm. We show that the general rules for the evaluation of these correlation functions take an especially simple form within the framework of directed loops. These rules state that contributing loops have to close coherently. As an application we evaluate the specific heat for the case of spin chains and ladders.Comment: For publication EPJ

Magnetic Raman scattering of the ordered tetrahedral spin-1/2 clusters in Cu_2Te_2O_5(Br_(1-x)Cl_x)_2 compounds

Raman light-scattering experiments in the antiferromagnetic phase of the Cu_2Te_2O_5(Br_(1-x)Cl_x)_2 compounds are analyzed in terms of a dimerized spin model for the tetrahedral Cu-clusters. It is shown that the longitudinal magnetic excitation in the pure Br system hybridizes with a localized singlet excitation due to the presence of a Dzyaloshinskii-Moriya anisotropy term. The drastic change of the magnetic scattering intensities observed when a proportion of Br is replaced by Cl ions, is proposed to be caused by a change of the magnetic order parameter. Instead of being parallel/antiparallel with each other, the spins in the two pairs of spin-1/2 order perpendicular to each other, when the composition x is larger than about 0.25.Comment: EPL, in pres

Control of the finite size corrections in exact diagonalization studies

We study the possibility of controlling the finite size corrections in exact diagonalization studies quantitatively. We consider the one- and two dimensional Hubbard model. We show that the finite-size corrections can be be reduced systematically by a grand-canonical integration over boundary conditions. We find, in general, an improvement of one order of magnitude with respect to studies with periodic boundary conditions only. We present results for ground-state properties of the 2D Hubbard model and an evaluation of the specific heat for the 1D and 2D Hubbard model.Comment: Phys. Rev. B (Brief Report), in pres

Spin-charge separation at small lengthscales in the 2D t-J model

We consider projected wavefunctions for the 2D $t-J$ model. For various wavefunctions, including correlated Fermi-liquid and Luttinger-type wavefunctions we present the static charge-charge and spin-spin structure factors. Comparison with recent results from a high-temperature expansion by Putikka {\it et al.} indicates spin-charge separation at small lengthscales.Comment: REVTEX, 5 pages, 5 figures hardcopies availabl

Vertex routing models

A class of models describing the flow of information within networks via routing processes is proposed and investigated, concentrating on the effects of memory traces on the global properties. The long-term flow of information is governed by cyclic attractors, allowing to define a measure for the information centrality of a vertex given by the number of attractors passing through this vertex. We find the number of vertices having a non-zero information centrality to be extensive/sub-extensive for models with/without a memory trace in the thermodynamic limit. We evaluate the distribution of the number of cycles, of the cycle length and of the maximal basins of attraction, finding a complete scaling collapse in the thermodynamic limit for the latter. Possible implications of our results on the information flow in social networks are discussed.Comment: 12 pages, 6 figure

Intrinsic adaptation in autonomous recurrent neural networks

A massively recurrent neural network responds on one side to input stimuli and is autonomously active, on the other side, in the absence of sensory inputs. Stimuli and information processing depends crucially on the qualia of the autonomous-state dynamics of the ongoing neural activity. This default neural activity may be dynamically structured in time and space, showing regular, synchronized, bursting or chaotic activity patterns. We study the influence of non-synaptic plasticity on the default dynamical state of recurrent neural networks. The non-synaptic adaption considered acts on intrinsic neural parameters, such as the threshold and the gain, and is driven by the optimization of the information entropy. We observe, in the presence of the intrinsic adaptation processes, three distinct and globally attracting dynamical regimes, a regular synchronized, an overall chaotic and an intermittent bursting regime. The intermittent bursting regime is characterized by intervals of regular flows, which are quite insensitive to external stimuli, interseeded by chaotic bursts which respond sensitively to input signals. We discuss these finding in the context of self-organized information processing and critical brain dynamics.Comment: 24 pages, 8 figure

‘Big Think’, Disjointed Incrementalism: Chinese Economic Success and Policy Lessons for Africa, or the Case for Pan-Africanism

Chinese economic success is not the product of free market accidental coincidence. Rather, it is orchestrated by the State through a mixture of nationalism (‘big think’) and pragmatic decisions (disjointed incrementalism) in agriculture, finance and industry. Furthermore, these decisions build upon existing institutions (e.g. the Household Responsibility System, Township Village Enterprises, etc), some dating back to pre-revolutionary China (e.g. Special Economic Zones), rather than imported ones from outside China. The article explores the utility (and lack thereof) of the Chinese model in the African context, as well as the possibilities of an Africa-centred ‘big think’ (Pan-Africanism) capable of mobilizing the continent for development