Classification of incomplete feature vectors by radial basis function networks

The paper describes the use of radial basis function neural networks with Gaussian basis functions to classify incomplete feature vectors. The method uses the fact that any marginal distribution of a Gaussian distribution can be determined from the mean vector and covariance matrix of the joint distribution.

Does this case hold the answer to one of the worse types of pain in medicine--that of loin pain haematuria syndrome (LPHS)

A patient with loin pain haematuria syndrome suffering chronic throbbing pulsing pain overlaid with prolonged periods of incapacitating colic and overnight vomiting was presented 10 months following diagnosis. Ultrasound was normal. No renal or ureteral stones, or filling defects were seen on CT. At cytoscopy, bladder and urethra were normal, and bloody urine effluxed from the left ureteric orifice. The ureters were normal at diagnosis, and developed new abutting non‐penetrating calcifications by 8 months. Pain episodes of complete incapacitating intensity of 2–4 h duration were reduced to 10 min with 5 mg crushed tadalafil administered at onset. If tadalafil was delayed to after onset, the original course of agony resulted. Daily tadalafil reduced loin pain intensity, but not the exacerbations. Tadalafil efficacy may indicate that the pain exacerbations are due to spasm of ureter smooth muscle. 5 mg tadalafil taken at onset alleviated severe loin pain exacerbations in this case of loin pain haematuria syndrome.

Uncovering Dependence Clusters and Linchpin Functions

Dependence clusters are (maximal) collections of mutually dependent source code entities according to some dependence relation. Their presence in software complicates many maintenance activities including testing, refactoring, and feature extraction. Despite several studies finding them common in production code, their formation, identification, and overall structure are not well understood, partly because of challenges in approximating true dependences between program entities. Previous research has considered two approximate dependence relations: a fine-grained statement-level relation using control and data dependences from a program’s System Dependence Graph and a coarser relation based on function-level controlflow reachability. In principal, the first is more expensive and more precise than the second. Using a collection of twenty programs, we present an empirical investigation of the clusters identified by these two approaches. In support of the analysis, we consider hybrid cluster types that works at the coarser function-level but is based on the higher-precision statement-level dependences. The three types of clusters are compared based on their slice sets using two clustering metrics. We also perform extensive analysis of the programs to identify linchpin functions – functions primarily responsible for holding a cluster together. Results include evidence that the less expensive, coarser approaches can often be used as e�ective proxies for the more expensive, finer-grained approaches. Finally, the linchpin analysis shows that linchpin functions can be e�ectively and automatically identified.