Neural network simulations on a parallel architecture are reported. The architecture is scalable and flexible enough to be useful for simulating various kinds of networks and paradigms. The computing device is based on an existing coarse-grain parallel framework (INMOS transputers), improved with finer-grain parallel abilities through VLSI chips, and is called the Lneuro 1.0 (for LEP neuromimetic) circuit. The modular architecture of the circuit makes it possible to build various kinds of boards to match the expected range of applications or to increase the power of the system by adding more hardware. The resulting machine remains reconfigurable to accommodate a specific problem to some extent. A small-scale machine has been realized using 16 Lneuros, to experimentally test the behavior of this architecture. Results are presented on an integer version of Kohonen feature maps. The speedup factor increases regularly with the number of clusters involved (to a factor of 80). Some ways to improve this family of neural network simulation machines are also investigated.
Two mathematical models of the COVID-19 dynamics are considered as the health system in some country consists in a network of regional hospital centers. The first
macroscopic
model for the virus dynamics at the level of the general population of the country is derived from a standard SIR model. The second
local
model refers to a single node of the health system network, i.e. it models the flows of patients with a smaller granularity at the level of a regional hospital care center for COVID-19 infected patients. Daily (low cost) data are easily collected at this level, and are worked out for a
fast evaluation
of the local health status thanks to control systems methods.
Precisely, the identifiability of the parameters of the hospital model is proven and thanks to the availability of clinical data, essential characteristics of the local health status are identified. Those parameters are meaningful not only to alert on some increase of the infection, but also to assess the efficiency of the therapy and health policy.
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