The public sector comprises government agencies, ministries, education institutions, health providers and other types of government, commercial and not-for-profit organisations. Unlike commercial enterprises, this environment is highly heterogeneous in all aspects. This forms a complex network which is not always optimised. A lack of optimisation and communication hinders information sharing between the network nodes limiting the flow of information. Another limiting aspect is privacy of personal information and security of operations of some nodes or segments of the network. Attempts to reorganise the network or improve communications to make more information available for sharing and analysis may be hindered or completely halted by public concerns over privacy, political agendas, social and technological barriers. This paper discusses a technical solution for information sharing while addressing the privacy concerns with no need for reorganisation of the existing public sector infrastructure . The solution is based on imposing an additional layer of Intelligent Software Agents and Knowledge Bases for data mining and analysis.

This paper describes how realistic neuromorphic networks can have their connectivity properties fully characterized in analytical fashion. By assuming that all neurons have the same shape and are regularly distributed along the two-dimensional orthogonal lattice with parameter $\Delta$, it is possible to obtain the accurate number of connections and cycles of any length from the autoconvolution function as well as from the respective spectral density derived from the adjacency matrix. It is shown that neuronal shape plays an important role in defining the spatial spread of network connections. In addition, most such networks are characterized by the interesting phenomenon where the connections are progressively shifted along the spatial domain where the network is embedded. It is also shown that the number of cycles follows a power law with their respective length. Morphological measurements for characterization of the spatial distribution of connections, including the adjacency matrix spectral density and the lacunarity of the connections, are suggested. The potential of the proposed approach is illustrated with respect to digital images of real neuronal cells.

Many complex networks have an underlying modular structure which can be identified by means of a variety of algorithms. The modularity Q has been introduced as a measure to assess the quality of clusterizations. Q has a global view, while in many real-world networks clusters are linked mainly locally among each other (local cluster-connectivity). Here, we introduce a new measure based on a local criterion for clusters, the goodness deviation G. Comparison of the two quantities shows that the goodness deviation is more robust than Q and appropriate for local cluster-connectivity networks. Moreover, G is able to reveal non-modular structure of a network.

The problem of making sequential decisions in unknown probabilistic environments is studied. In cycle $t$ action $y_t$ results in perception $x_t$ and reward $r_t$, where all quantities in general may depend on the complete history. The perception $x_t$ and reward $r_t$ are sampled from the (reactive) environmental probability distribution $\mu$. This very general setting includes, but is not limited to, (partial observable, k-th order) Markov decision processes. Sequential decision theory tells us how to act in order to maximize the total expected reward, called value, if $\mu$ is known. Reinforcement learning is usually used if $\mu$ is unknown. In the Bayesian approach one defines a mixture distribution $\xi$ as a weighted sum of distributions $\nu\in\M$, where $\M$ is any class of distributions including the true environment $\mu$. We show that the Bayes-optimal policy $p^\xi$ based on the mixture $\xi$ is self-optimizing in the sense that the average value converges asymptotically for all $\mu\in\M$ to the optimal value achieved by the (infeasible) Bayes-optimal policy $p^\mu$ which knows $\mu$ in advance. We show that the necessary condition that $\M$ admits self-optimizing policies at all, is also sufficient. No other structural assumptions are made on $\M$. As an example application, we discuss ergodic Markov decision processes, which allow for self-optimizing policies. Furthermore, we show that $p^\xi$ is Pareto-optimal in the sense that there is no other policy yielding higher or equal value in {\em all} environments $\nu\in\M$ and a strictly higher value in at least one. (18kb)

In order to detect patterns in real networks, randomized graph ensembles that preserve only part of the topology of an observed network are systematically used as fundamental null models. However, their generation is still problematic. The existing approaches are either computationally demanding and beyond analytic control, or analytically accessible but highly approximate. Here we propose a solution to this long-standing problem by introducing an exact and fast method that allows to obtain expectation values and standard deviations of any topological property analytically, for any binary, weighted, directed or undirected network. Remarkably, the time required to obtain the expectation value of any property is as short as that required to compute the same property on the single original network. Our method reveals that the null behavior of various correlation properties is different from what previously believed, and highly sensitive to the particular network considered. Moreover, our approach shows that important structural properties (such as the modularity used in community detection problems) are currently based on incorrect expressions, and provides the exact quantities that should replace them.