309,879 research outputs found
Backbone of credit relationships in the Japanese credit market
We detect the backbone of the weighted bipartite network of the Japanese
credit market relationships. The backbone is detected by adapting a general
method used in the investigation of weighted networks. With this approach we
detect a backbone that is statistically validated against a null hypothesis of
uniform diversification of loans for banks and firms. Our investigation is done
year by year and it covers more than thirty years during the period from 1980
to 2011. We relate some of our findings with economic events that have
characterized the Japanese credit market during the last years. The study of
the time evolution of the backbone allows us to detect changes occurred in
network size, fraction of credit explained, and attributes characterizing the
banks and the firms present in the backbone.Comment: 14 pages, 8 figure
Multiple dynamical time-scales in networks with hierarchically nested modular organization
Many natural and engineered complex networks have intricate mesoscopic
organization, e.g., the clustering of the constituent nodes into several
communities or modules. Often, such modularity is manifested at several
different hierarchical levels, where the clusters defined at one level appear
as elementary entities at the next higher level. Using a simple model of a
hierarchical modular network, we show that such a topological structure gives
rise to characteristic time-scale separation between dynamics occurring at
different levels of the hierarchy. This generalizes our earlier result for
simple modular networks, where fast intra-modular and slow inter-modular
processes were clearly distinguished. Investigating the process of
synchronization of oscillators in a hierarchical modular network, we show the
existence of as many distinct time-scales as there are hierarchical levels in
the system. This suggests a possible functional role of such mesoscopic
organization principle in natural systems, viz., in the dynamical separation of
events occurring at different spatial scales.Comment: 10 pages, 4 figure
Social and interactional practices for disseminating current awareness information in an organisational setting.
Current awareness services are designed to keep users informed about recent developments based around user need profiles. In organisational settings, they may operate through both electronic and social interactions aimed at delivering information that is relevant, pertinent and current. Understanding these interactions can reveal the tensions in current awareness dissemination and help inform ways of making services more effective and efficient. We report an in-depth, observational study of electronic current awareness use within a large London law firm. The study found that selection, re-aggregation and forwarding of information by multiple actors gives rise to a complex sociotechnical distribution network. Knowledge management staff act as a layer of “intelligent filters” sensitive to complex, local information needs; their distribution decisions address multiple situational relevance factors in a situation fraught with information overload and restrictive time-pressures. Their decisions aim to optimise conflicting constraints of recall, precision and information quantity. Critical to this is the use of dynamic profile updates which propagate back through the network through formal and informal social interactions. This supports changes to situational relevance judgements and so allows the network to ‘self-tune’. These findings lead to design requirements, including that systems should support rapid assessment of information items against an individual’s interests; that it should be possible to organise information for different subsequent uses; and that there should be back-propagation from information consumers to providers, to tune the understanding of their information needs
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
Existing FNNs are mostly developed under a shallow network configuration
having lower generalization power than those of deep structures. This paper
proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be
automatically extracted from data streams or removed if they play limited role
during their lifespan. The structure of the network can be deepened on demand
by stacking additional layers using a drift detection method which not only
detects the covariate drift, variations of input space, but also accurately
identifies the real drift, dynamic changes of both feature space and target
space. DEVFNN is developed under the stacked generalization principle via the
feature augmentation concept where a recently developed algorithm, namely
gClass, drives the hidden layer. It is equipped by an automatic feature
selection method which controls activation and deactivation of input attributes
to induce varying subsets of input features. A deep network simplification
procedure is put forward using the concept of hidden layer merging to prevent
uncontrollable growth of dimensionality of input space due to the nature of
feature augmentation approach in building a deep network structure. DEVFNN
works in the sample-wise fashion and is compatible for data stream
applications. The efficacy of DEVFNN has been thoroughly evaluated using seven
datasets with non-stationary properties under the prequential test-then-train
protocol. It has been compared with four popular continual learning algorithms
and its shallow counterpart where DEVFNN demonstrates improvement of
classification accuracy. Moreover, it is also shown that the concept drift
detection method is an effective tool to control the depth of network structure
while the hidden layer merging scenario is capable of simplifying the network
complexity of a deep network with negligible compromise of generalization
performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System
Relevance of Dynamic Clustering to Biological Networks
Network of nonlinear dynamical elements often show clustering of
synchronization by chaotic instability. Relevance of the clustering to
ecological, immune, neural, and cellular networks is discussed, with the
emphasis of partially ordered states with chaotic itinerancy. First, clustering
with bit structures in a hypercubic lattice is studied. Spontaneous formation
and destruction of relevant bits are found, which give self-organizing, and
chaotic genetic algorithms. When spontaneous changes of effective couplings are
introduced, chaotic itinerancy of clusterings is widely seen through a feedback
mechanism, which supports dynamic stability allowing for complexity and
diversity, known as homeochaos. Second, synaptic dynamics of couplings is
studied in relation with neural dynamics. The clustering structure is formed
with a balance between external inputs and internal dynamics. Last, an
extension allowing for the growth of the number of elements is given, in
connection with cell differentiation. Effective time sharing system of
resources is formed in partially ordered states.Comment: submitted to Physica D, no figures include
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