Bounds on the diameter of Cayley graphs of the symmetric group

In this paper we are concerned with the conjecture that, for any set of generators S of the symmetric group \(\operatorname {Sym}(n)\) , the word length in terms of S of every permutation is bounded above by a polynomial of n. We prove this conjecture for sets of generators containing a permutation fixing at least 37 % of the points.     

CanSAS‐IV

A two-day workshop on beamline integration and data formatting (HDF5/NeXus) of the EIGER detector was held in Baden, Switzerland, January 24–25, 2013. Its aim was to discuss the technical challenges inherent with the next generation of high-frame-rate, high-resolution X-ray imaging detectors, and specifically with the EIGER detector. EIGER is a photon-counting hybrid pixel detector developed at the Paul Scherrer Institute (PSI) and DECTRIS Ltd. With even higher spatial resolution and frame rates than its predecessor, the PILATUS detector, it will be able to continuously produce up to 3000 images per second. The corresponding extreme data rates generated by this and future detectors present a significant challenge for beamline integration of the detectors, for data handling by the users, and for data processing software. Efficient data flow, storage, and processing must be achieved to handle the huge data sets that will be produced in seconds by these devices.     

Sensitivity to point-spread function parameters in medical ultrasound image deconvolution

Clinical ultrasound images are often perceived as difficult to interpret due to image blurring and speckle inherent in the ultrasound imaging. But the image quality can be improved by deconvolution using an estimate of the point-spread function. However, it is difficult to obtain a sufficiently accurate estimate of the point-spread function in vivo because of the unknown properties of the soft tissue in clinical applications. Local variations in the speed of sound and attenuation change the pulse and beam shape. These in turn affect the point-spread function. The purpose and novelty of this paper is therefore to explore the sensitivity of a state-of-the-art deconvolution algorithm to uncertainty in the point-spread function. The point-spread function in our restoration algorithm is made shift invariant in the lateral dimension but shift dependent in the axial direction, and is modelled to match a 128-element 1D linear array often found in clinical use. We present simulated and in vitro sensitivity analyses of two-dimensional deconvolution while varying six parameters on which the point-spread function depends. Uncertainty in the ultrasound machine is analysed by varying the axial depths of lateral and elevational foci alongside height and width of transducer elements. Sensitivity to tissue influence is investigated by varying the speed of sound and frequency-dependent attenuation of the electro-mechanical impulse response. The results are analysed both quantitatively and in terms of the perceived image quality. First, the assessment of deconvolution using the logarithmic image amplitude is found to be a better indicator of the perceived improvement in the restoration. Secondly, the two most critical parameters for two-dimensional deconvolution are discovered to be the lateral focus and the speed of sound, because the success of deconvolution is perceived primarily in terms of deblurring. We also observed similar patterns for the simulation and in vitro experiment. Finally, we show that it is possible to restore in vivo ultrasound images using an assumed point-spread function and hence conclude that an exact point-spread function is not necessary for enhancing ultrasound image quality by deconvolution.     

Semi‐analytical method for departure point determination

A new method for departure point determination on Cartesian grids, the semi‐analytical upwind path line tracing (SUT) method, is presented and compared to two typical departure point determination methods used in semi‐Lagrangian advection schemes, the Euler method and the four‐step Runge–Kutta method. Rigorous comparisons of the three methods were conducted for a severely curving hypothetical flow field and for advective transport in the rotation of a Gaussian concentration hill. The SUT method was shown to have equivalent accuracy to the Runge–Kutta method but with significantly improved computational efficiency. Depending on the case being simulated, the SUT method provides either far greater or equivalent computational efficiency and more certain accuracy than the Euler method. Copyright © 2004 John Wiley & Sons, Ltd.     

Quantum Bayesian computation

Quantum Bayesian computation is an emerging field that levers the computational gains available from quantum computers. They promise to provide an exponential speed-up in Bayesian computation. Our article adds to the literature in three ways. First, we describe how quantum von Neumann measurement provides quantum versions of popular machine learning algorithms such as Markov chain Monte Carlo and deep learning that are fundamental to Bayesian learning. Second, we describe quantum data encoding methods needed to implement quantum machine learning including the counterparts to traditional feature extraction and kernel embeddings methods. Third, we show how quantum algorithms naturally calculate Bayesian quantities of interest such as posterior distributions and marginal likelihoods. Our goal then is to show how quantum algorithms solve statistical machine learning problems. On the theoretical side, we provide quantum versions of high dimensional regression, Gaussian processes and stochastic gradient descent. On the empirical side, we apply a quantum FFT algorithm to Chicago house price data. Finally, we conclude with directions for future research.     

Structure Elucidation and Activity of Kolossin A,the D‐/L‐Pentadecapeptide Product of a Giant Nonribosomal Peptide Synthetase

The largest continuous bacterial nonribosomal peptide synthetase discovered so far is described. It consists of 15 consecutive modules arising from an uninterrupted, fully functional gene in the entomopathogenic bacterium Photorhabdus luminescens. The identification of its cryptic biosynthesis product was achieved by using a combination of genome analysis, promoter exchange, isotopic labeling experiments, and total synthesis of a focused collection of peptide candidates. Although it belongs to the growing class of D ‐/ L ‐peptide natural products, the encoded metabolite kolossin A was found to be largely devoid of antibiotic activity and is likely involved in interspecies communication. A stereoisomer of this peculiar natural product displayed high activity against Trypanosoma brucei rhodesiense, a recalcitrant parasite that causes the deadly disease African sleeping sickness.     

Symmetry calculation for molecules and transition states

The symmetry of molecules and transition states of elementary reactions is an essential property with important implications for computational chemistry. The automated identification of symmetry by computers is a very useful tool for many applications, but often relies on the availability of three‐dimensional coordinates of the atoms in the molecule and hence becomes less useful when these coordinates are a priori unavailable. This article presents a new algorithm that identifies symmetry of molecules and transition states based on an augmented graph representation of the corresponding structures, in which both topology and the presence of stereocenters are accounted for. The automorphism group order of the graph associated with the molecule or transition state is used as a starting point. A novel concept of label‐stereoisomers, that is, stereoisomers that arise after labeling homomorph substituents in the original molecule so that they become distinguishable, is introduced and used to obtain the symmetry number. The algorithm is characterized by its generic nature and avoids the use of heuristic rules that would limit the applicability. The calculated symmetry numbers are in agreement with expected values for a large and diverse set of structures, ranging from asymmetric, small molecules such as fluorochlorobromomethane to highly symmetric structures found in drug discovery assays. The new algorithm opens up new possibilities for the fast screening of the degree of symmetry of large sets of molecules. © 2014 Wiley Periodicals, Inc.     

Investigation of confined ionic liquid in nanostructured materials by a combination of SANS, contrast-matching SANS, and nitrogen adsorption

Small-angle neutron scattering (SANS), contrast-matching SANS, and nitrogen adsorption have been utilized to investigate the confined ionic liquid (IL) [bmim][PF(6)] phase in ordered mesoporous silica MCM-41 and SBA-15. The results suggest that the pores of SBA-15 are completely filled with IL whereas a small fraction of the pore volume, the pore "core", of MCM-41 is empty. The contrast-matching SANS measurements confirm the enhanced solubility of water in IL. In addition, they provide strong evidence that water does not enter the empty pore core of MCM-41, possibly because of the preferred orientation of the IL molecules in the adsorbed layer.