Benchmark Status in Fixed-Income Asset Markets

What is a benchmark bond? We provide a formal theoretical treatment of this concept that relates endogenously determined benchmark status to the location of price discovery and we derive its implications. We describe a rich but little used econometric technique for identifying the benchmark that is congruent with our theoretical framework. We apply this in the context of the US corporate bond market and to the natural experiment that occurred when benchmark status was contested in the European sovereign bond markets after the introduction of the Euro. We show that France provides the benchmark at most maturities in the Euro-denominated sovereign bond market and that IBM provides the benchmark in the 10 year maturity in the US corporate bond market.

Using idiothetic cues to swim a path with a fixed trajectory and distance: Necessary involvement of the hippocampus but not the retrosplenial cortex.

Rats rapidly learned to find a submerged platform in a water maze at a constant distance and angle from the start point, which changed on every trial. The rats performed accurately in the light and dark, but prior rotation disrupted the latter condition. The rats were then retested after receiving cytotoxic hippocampal or retrosplenial cortex lesions. Retrosplenial lesions had no apparent effect in either the light or dark. Hippocampal lesions impaired performance in both conditions but spared the ability to locate a platform placed in the center of the pool. A hippocampal deficit emerged when this pool-center task was run in the dark. The spatial effects of hippocampal damage extend beyond allocentric tasks to include aspects of idiothetic guidance.

Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network

An artificial neural network (ANN) model is developed for the analysis and simulation of the correlation between the properties of maraging steels and composition, processing and working conditions. The input parameters of the model consist of alloy composition, processing parameters (including cold deformation degree, ageing temperature, and ageing time), and working temperature. The outputs of the ANN model include property parameters namely: ultimate tensile strength, yield strength, elongation, reduction in area, hardness, notched tensile strength, Charpy impact energy, fracture toughness, and martensitic transformation start temperature. Good performance of the ANN model is achieved. The model can be used to calculate properties of maraging steels as functions of alloy composition, processing parameters, and working condition. The combined influence of Co and Mo on the properties of maraging steels is simulated using the model. The results are in agreement with experimental data. Explanation of the calculated results from the metallurgical point of view is attempted. The model can be used as a guide for further alloy development.

Artificial Intelligence and Cognitive Science

Editorial for 17th AICS Conference