Amedeo Buonanno

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Glycosylated albumin and glycosylated protein in serum were measured in 4 well-controlled diabetic dogs, 4 poorly controlled diabetic dogs, and 21 nondiabetic dogs. Concentrations of both glycosylated components in the well-controlled dogs were similar to those in nondiabetic dogs. Serum concentrations of glycosylated albumin and protein in the poorly(More)
A Salter-Harris type-II fracture of the distal portion of the femur in a 1-year-old Pony of America was repaired by use of lateral plating combined with interfragmentary compression. The configuration of the fracture and the method of internal fixation with a condylar buttress plate were unique and resulted in primary bone healing, seen at the 5-month(More)
In modeling time series, convolution multi-layer graphs are able to capture long-term dependence at a gradually increasing scale. We present an approach to learn a layered factor graph architecture starting from a stationary latent models for each layer. Simulations of belief propagation are reported for a three-layer graph on a small data set of characters.
We propose a Multi-Layer Network based on the Bayesian framework of the Factor Graphs in Reduced Normal Form (FGrn) applied to a two-dimensional lattice. The Latent Variable Model (LVM) is the basic building block of a quadtree hierarchy built on top of a bottom layer of random variables that represent pixels of an image, a feature map, or more generally a(More)
Portosystemic shunt was diagnosed in a 6-month-old Quarter Horse filly with acute onset of apparent blindness and a 3-month history of depression, lethargy, and ataxia. Clinicopathologic test results indicated slightly high gamma-glutamyl transpeptidase activity and serum total bilirubin concentration. Sulfobromophthalein half time was prolonged, and plasma(More)
We apply belief propagation to a Bayesian bipartite graph composed of discrete independent hidden variables and discrete visible variables. The network is the Discrete counterpart of Independent Component Analysis (DICA) and it is manipulated in a factor graph form for inference and learning. A full set of simulations is reported for character images from(More)
We build a multi-layer architecture using the Bayesian framework of the Factor Graphs in Reduced Normal Form (FGrn). This model allows great modularity and unique localized learning equations. The multi-layer architecture implements a hierarchical data representation that via belief propagation can be used for learning and inference in pattern completion,(More)
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