Proton-proton correlations observed in two-proton radioactivity of 94Ag

The stability and spontaneous decay of naturally occurring atomic nuclei have been much studied ever since Becquerel discovered natural radioactivity in 1896. In 1960, proton-rich nuclei with an odd or an even atomic number Z were predicted to decay through one- and two-proton radioactivity, respectively. The experimental observation of one-proton radioactivity was first reported in 1982, and two-proton radioactivity has now also been detected by experimentally studying the decay properties of 45Fe (refs 3, 4) and 54Zn (ref. 5). Here we report proton-proton correlations observed during the radioactive decay of a spinning long-lived state of the lightest known isotope of silver, 94Ag, which is known to undergo one-proton decay. We infer from these correlations that the long-lived state must also decay through simultaneous two-proton emission, making 94Ag the first nucleus to exhibit one- as well as two-proton radioactivity. We attribute the two-proton emission behaviour and the unexpectedly large probability for this decay mechanism to a very large deformation of the parent nucleus into a prolate (cigar-like) shape, which facilitates emission of protons either from the same or from opposite ends of the 'cigar'.     

A Machine Learning Approach for Mechanism Selection in Complex Negotiations

Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. A variety of automated negotiation mechanisms have been proposed in the literature. The effectiveness of those mechanisms, however, may depend on the characteristics of the underlying negotiation problem (e.g. on the complexity of participant’s utility functions, as well as the degree of conflict between participants). While one mechanism may be a good choice for a negotiation problem, it may be a poor choice for another. In this paper, we pursue the problem of selecting the most effective negotiation mechanism given a particular problem by (1) defining a set of scenario metrics to capture the relevant features of negotiation problems, (2) evaluating the performance of a range of negotiation mechanisms on a diverse test suite of negotiation scenarios, (3) applying machine learning techniques to identify which mechanisms work best with which scenarios, and (4) demonstrating that using these classification rules for mechanism selection enables significantly better negotiation performance than any single mechanism alone.