Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

Smart premise selection is essential when using automated reasoning as a tool for large-theory formal proof development. A good method for premise selection in complex mathematical libraries is the application of machine learning to large corpora of proofs. This work develops learning-based premise selection in two ways. First, a newly available minimal dependency analysis of existing high-level formal mathematical proofs is used to build a large knowledge base of proof dependencies, providing precise data for ATP-based re-verification and for training premise selection algorithms. Second, a new machine learning algorithm for premise selection based on kernel methods is proposed and implemented. To evaluate the impact of both techniques, a benchmark consisting of 2078 large-theory mathematical problems is constructed,extending the older MPTP Challenge benchmark. The combined effect of the techniques results in a 50% improvement on the benchmark over the Vampire/SInE state-of-the-art system for automated reasoning in large theories.Comment: 26 page

Pairing correlations of cold fermionic gases at overflow from a narrow to a wide harmonic trap

Within the context of Hartree-Fock-Bogoliubov theory, we study the behavior of superfluid Fermi systems when they pass from a small to a large container. Such systems can be now realized thanks to recent progress in experimental techniques. It will allow to better understand pairing properties at overflow and in general in rapidly varying external potentials

Two-fluid model for a rotating trapped Fermi gas in the BCS phase

We investigate the dynamical properties of a superfluid gas of trapped fermionic atoms in the BCS phase. As a simple example we consider the reaction of the gas to a slow rotation of the trap. It is shown that the currents generated by the rotation can be understood within a two-fluid model similar to the one used in the theory of superconductors, but with a position dependent ratio of normal and superfluid densities. The rather general result of this paper is that already at very low temperatures, far below the critical one, an important normal-fluid component appears in the outer regions of the gas. This renders the experimental observation of superfluidity effects more difficult and indicates that reliable theoretical predictions concerning other dynamical properties, like the frequencies of collective modes, can only be made by taking into account temperature effects.Comment: 6 pages, 4 figure

Electronic and atomic shell structure in aluminum nanowires

We report experiments on aluminum nanowires in ultra-high vacuum at room temperature that reveal a periodic spectrum of exceptionally stable structures. Two "magic" series of stable structures are observed: At low conductance, the formation of stable nanowires is governed by electronic shell effects whereas for larger contacts atomic packing dominates. The crossover between the two regimes is found to be smooth. A detailed comparison of the experimental results to a theoretical stability analysis indicates that while the main features of the observed electron-shell structure are similar to those of alkali and noble metals, a sequence of extremely stable wires plays a unique role in Aluminum. This series appears isolated in conductance histograms and can be attributed to "superdeformed" non-axisymmetric nanowires.Comment: 15 pages, 9 figure

A Four-Factor User Interaction Model for Content-Based Image Retrieval

In order to bridge the “Semantic gap”, a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques in most existing CBIR systems still lack satisfactory user interaction although some work has been done to improve the interaction as well as the search accuracy. In this paper, we propose a four-factor user interaction model and investigate its effects on CBIR by an empirical evaluation. Whilst the model was developed for our research purposes, we believe the model could be adapted to any content-based search system