15,362 research outputs found
Walking across Wikipedia: a scale-free network model of semantic memory retrieval.
Semantic knowledge has been investigated using both online and offline methods. One common online method is category recall, in which members of a semantic category like "animals" are retrieved in a given period of time. The order, timing, and number of retrievals are used as assays of semantic memory processes. One common offline method is corpus analysis, in which the structure of semantic knowledge is extracted from texts using co-occurrence or encyclopedic methods. Online measures of semantic processing, as well as offline measures of semantic structure, have yielded data resembling inverse power law distributions. The aim of the present study is to investigate whether these patterns in data might be related. A semantic network model of animal knowledge is formulated on the basis of Wikipedia pages and their overlap in word probability distributions. The network is scale-free, in that node degree is related to node frequency as an inverse power law. A random walk over this network is shown to simulate a number of results from a category recall experiment, including power law-like distributions of inter-response intervals. Results are discussed in terms of theories of semantic structure and processing
Order and disorder in everyday action: the roles of contention scheduling and supervisory attention
This paper describes the contention scheduling/supervisory attentional system approach to action selection and uses this account to structure a survey of current theories of the control of action. The focus is on how such theories account for the types of error produced by some patients with frontal and/or left temporoparietal damage when attempting everyday tasks. Four issues, concerning both the theories and their accounts of everyday action breakdown, emerge: first, whether multiple control systems, each capable of controlling action in different situations, exist; second, whether different forms of damage at the neural level result in conceptually distinct disorders; third, whether semantic/conceptual knowledge of objects and actions can be dissociated from control mechanisms, and if so what computational principles govern sequential control; and fourth, whether disorders of everyday action should be attributed to a loss of semantic/conceptual knowledge, a malfunction of control, or some combination of the two
Intelligent search for distributed information sources using heterogeneous neural networks
As the number and diversity of distributed information sources on the Internet exponentially increase, various search services are developed to help the users to locate relevant information. But they still exist some drawbacks such as the difficulty of mathematically modeling retrieval process, the lack of adaptivity and the indiscrimination of search. This paper shows how heteroge-neous neural networks can be used in the design of an intelligent distributed in-formation retrieval (DIR) system. In particular, three typical neural network models - Kohoren's SOFM Network, Hopfield Network, and Feed Forward Network with Back Propagation algorithm are introduced to overcome the above drawbacks in current research of DIR by using their unique properties. This preliminary investigation suggests that Neural Networks are useful tools for intelligent search for distributed information sources
Data on face-to-face contacts in an office building suggests a low-cost vaccination strategy based on community linkers
Empirical data on contacts between individuals in social contexts play an
important role in providing information for models describing human behavior
and how epidemics spread in populations. Here, we analyze data on face-to-face
contacts collected in an office building. The statistical properties of
contacts are similar to other social situations, but important differences are
observed in the contact network structure. In particular, the contact network
is strongly shaped by the organization of the offices in departments, which has
consequences in the design of accurate agent-based models of epidemic spread.
We consider the contact network as a potential substrate for infectious disease
spread and show that its sparsity tends to prevent outbreaks of rapidly
spreading epidemics. Moreover, we define three typical behaviors according to
the fraction of links each individual shares outside its own department:
residents, wanderers and linkers. Linkers () act as bridges in the
network and have large betweenness centralities. Thus, a vaccination strategy
targeting linkers efficiently prevents large outbreaks. As such a behavior may
be spotted a priori in the offices' organization or from surveys, without the
full knowledge of the time-resolved contact network, this result may help the
design of efficient, low-cost vaccination or social-distancing strategies
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