Quantum-State Controlled Penning Ionization Reactions between Ultracold Alkali and Metastable Helium Atoms

In an ultracold, optically trapped mixture of $^{87}$Rb and metastable triplet $^4$He atoms we have studied trap loss for different spin-state combinations, for which interspecies Penning ionization is the main two-body loss process. We observe long trapping lifetimes for the purely quartet spin-state combination, indicating strong suppression of Penning ionization loss by at least two orders of magnitude. For the other spin-mixtures we observe short lifetimes that depend linearly on the doublet character of the entrance channel. We compare the extracted loss rate coefficient with recent predictions of multichannel quantum-defect theory for reactive collisions involving a strong exothermic loss channel and find near-universal loss for doublet scattering. Our work demonstrates control of reactive collisions by internal atomic state preparation.Comment: 5 pages, 5 figures + Supplemental Material

Efficient production of an 87Rb F = 2, mF = 2 Bose-Einstein condensate in a hybrid trap

We have realized Bose-Einstein condensation (BEC) of 87Rb in the F=2, m_F=2 hyperfine substate in a hybrid trap, consisting of a quadrupole magnetic field and a single optical dipole beam. The symmetry axis of the quadrupole magnetic trap coincides with the optical beam axis, which gives stronger axial confinement than previous hybrid traps. After loading 2x10^6 atoms at 14 muK from a quadrupole magnetic trap into the hybrid trap, we perform efficient forced evaporation and reach the onset of BEC at a temperature of 0.5 muK and with 4x10^5 atoms. We also obtain thermal clouds of 1x10^6 atoms below 1 muK in a pure single beam optical dipole trap, by ramping down the magnetic field gradient after evaporative cooling in the hybrid trap.Comment: 8 pages, 8 figures, improved on basis of referee comment

Finite mixtures of matrix-variate Poisson-log normal distributions for three-way count data

Three-way data structures, characterized by three entities, the units, the variables and the occasions, are frequent in biological studies. In RNA sequencing, three-way data structures are obtained when high-throughput transcriptome sequencing data are collected for n genes across p conditions at r occasions. Matrix-variate distributions offer a natural way to model three-way data and mixtures of matrix-variate distributions can be used to cluster three-way data. Clustering of gene expression data is carried out as means to discovering gene co-expression networks. In this work, a mixture of matrix-variate Poisson-log normal distributions is proposed for clustering read counts from RNA sequencing. By considering the matrix-variate structure, full information on the conditions and occasions of the RNA sequencing dataset is simultaneously considered, and the number of covariance parameters to be estimated is reduced. A Markov chain Monte Carlo expectation-maximization algorithm is used for parameter estimation and information criteria are used for model selection. The models are applied to both real and simulated data, giving favourable clustering results

New Central American and Mexican Enaphalodes Haldeman, 1847 (Coleoptera: Cerambycidae) with taxonomic notes and a key to species

A review of Enaphalodes Haldeman, 1847 is presented. Descriptions of four new species of Enaphalodes are included: E. antonkozlovi, sp. nov. from Costa Rica, E. bingkirki, sp. nov. from Nicaragua, E. monzoni, sp. nov. from Guatemala, and E. cunninghami, sp. nov. from Mexico. Enaphalodes senex (Bates, 1884) is revalidated and it is newly recorded from Nicaragua and Guatemala. A key to the 15 currently recognized species of Enaphalodes is included

Semantic variation operators for multidimensional genetic programming

Multidimensional genetic programming represents candidate solutions as sets of programs, and thereby provides an interesting framework for exploiting building block identification. Towards this goal, we investigate the use of machine learning as a way to bias which components of programs are promoted, and propose two semantic operators to choose where useful building blocks are placed during crossover. A forward stagewise crossover operator we propose leads to significant improvements on a set of regression problems, and produces state-of-the-art results in a large benchmark study. We discuss this architecture and others in terms of their propensity for allowing heuristic search to utilize information during the evolutionary process. Finally, we look at the collinearity and complexity of the data representations that result from these architectures, with a view towards disentangling factors of variation in application.Comment: 9 pages, 8 figures, GECCO 201