M\"{o}bius deconvolution on the hyperbolic plane with application to impedance density estimation

In this paper we consider a novel statistical inverse problem on the Poincar\'{e}, or Lobachevsky, upper (complex) half plane. Here the Riemannian structure is hyperbolic and a transitive group action comes from the space of $2\times2$ real matrices of determinant one via M\"{o}bius transformations. Our approach is based on a deconvolution technique which relies on the Helgason--Fourier calculus adapted to this hyperbolic space. This gives a minimax nonparametric density estimator of a hyperbolic density that is corrupted by a random M\"{o}bius transform. A motivation for this work comes from the reconstruction of impedances of capacitors where the above scenario on the Poincar\'{e} plane exactly describes the physical system that is of statistical interest.Comment: Published in at http://dx.doi.org/10.1214/09-AOS783 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Spin dynamics of FeGa3−x_{3-x}Gex_x studied by Electron Spin Resonance

The intermetallic semiconductor FeGa$_{3}$ acquires itinerant ferromagnetism upon electron doping by a partial replacement of Ga with Ge. We studied the electron spin resonance (ESR) of high-quality single crystals of FeGa$_{3-x}$Ge$_x$ for $x$ from 0 up to 0.162 where ferromagnetic order is observed. For $x = 0$ we observed a well-defined ESR signal, indicating the presence of pre-formed magnetic moments in the semiconducting phase. Upon Ge doping the occurrence of itinerant magnetism clearly affects the ESR properties below $\approx 40$~K whereas at higher temperatures an ESR signal as seen in FeGa$_{3}$ prevails independent on the Ge-content. The present results show that the ESR of FeGa$_{3-x}$Ge$_x$ is an appropriate and direct tool to investigate the evolution of 3d-based itinerant magnetism.Comment: 12 pages, 7 figure

Directing cell migration and organization via nanocrater-patterned cell-repellent interfaces.

Although adhesive interactions between cells and nanostructured interfaces have been studied extensively, there is a paucity of data on how nanostructured interfaces repel cells by directing cell migration and cell-colony organization. Here, by using multiphoton ablation lithography to pattern surfaces with nanoscale craters of various aspect ratios and pitches, we show that the surfaces altered the cells focal-adhesion size and distribution, thus affecting cell morphology, migration and ultimately localization. We also show that nanocrater pitch can disrupt the formation of mature focal adhesions to favour the migration of cells towards higher-pitched regions, which present increased planar area for the formation of stable focal adhesions. Moreover, by designing surfaces with variable pitch but constant nanocrater dimensions, we were able to create circular and striped cellular patterns. Our surface-patterning approach, which does not involve chemical treatments and can be applied to various materials, represents a simple method to control cell behaviour on surfaces

Graduate Piano Trio: Bon-Hee Koo, Piano; Taik-Ju Lee, Violin; Peter Garfield, Cello; December 4, 1974

Centennial East Recital HallWednesday EveningDecember 4, 19748:15 p.m

Improving representations of genomic sequence motifs in convolutional networks with exponential activations.

ABSTRACT Deep convolutional neural networks (CNNs) trained on regulatory genomic sequences tend to build representations in a distributed manner, making it a challenge to extract learned features that are biologically meaningful, such as sequence motifs. Here we perform a comprehensive analysis on synthetic sequences to investigate the role that CNN activations have on model interpretability. We show that employing an exponential activation to first layer filters consistently leads to interpretable and robust representations of motifs compared to other commonly used activations. Strikingly, we demonstrate that CNNs with better test performance do not necessarily imply more interpretable representations with attribution methods. We find that CNNs with exponential activations significantly improve the efficacy of recovering biologically meaningful representations with attribution methods. We demonstrate these results generalise to real DNA sequences across several in vivo datasets. Together, this work demonstrates how a small modification to existing CNNs, i.e. setting exponential activations in the first layer, can significantly improve the robustness and interpretabilty of learned representations directly in convolutional filters and indirectly with attribution methods

De novo assembly of potential linear artificial chromosome constructs capped with expansive telomeric repeats

<p>Abstract</p> <p>Background</p> <p>Artificial chromosomes (ACs) are a promising next-generation vector for genetic engineering. The most common methods for developing AC constructs are to clone and combine centromeric DNA and telomeric DNA fragments into a single large DNA construct. The AC constructs developed from such methods will contain very short telomeric DNA fragments because telomeric repeats can not be stably maintained in <it>Escherichia coli</it>.</p> <p>Results</p> <p>We report a novel approach to assemble AC constructs that are capped with long telomeric DNA. We designed a plasmid vector that can be combined with a bacterial artificial chromosome (BAC) clone containing centromeric DNA sequences from a target plant species. The recombined clone can be used as the centromeric DNA backbone of the AC constructs. We also developed two plasmid vectors containing short arrays of plant telomeric DNA. These vectors can be used to generate expanded arrays of telomeric DNA up to several kilobases. The centromeric DNA backbone can be ligated with the telomeric DNA fragments to generate AC constructs consisting of a large centromeric DNA fragment capped with expansive telomeric DNA at both ends.</p> <p>Conclusions</p> <p>We successfully developed a procedure that circumvents the problem of cloning and maintaining long arrays of telomeric DNA sequences that are not stable in <it>E. coli</it>. Our procedure allows development of AC constructs in different eukaryotic species that are capped with long and designed sizes of telomeric DNA fragments.</p

Bayesian structural identification of a long suspension bridge considering temperature and traffic load effects

© The Author(s) 2018. This article presents a probabilistic structural identification of the Tamar bridge using a detailed finite element model. Parameters of the bridge cables initial strain and bearings friction were identified. Effects of temperature and traffic were jointly considered as a driving excitation of the bridge’s displacement and natural frequency response. Structural identification is performed with a modular Bayesian framework, which uses multiple response Gaussian processes to emulate the model response surface and its inadequacy, that is, model discrepancy. In addition, the Metropolis–Hastings algorithm was used as an expansion for multiple parameter identification. The novelty of the approach stems from its ability to obtain unbiased parameter identifications and model discrepancy trends and correlations. Results demonstrate the applicability of the proposed method for complex civil infrastructure. A close agreement between identified parameters and test data was observed. Estimated discrepancy functions indicate that the model predicted the bridge mid-span displacements more accurately than its natural frequencies and that the adopted traffic model was less able to simulate the bridge behaviour during traffic congestion periods

Representation learning of genomic sequence motifs with convolutional neural networks.

Although convolutional neural networks (CNNs) have been applied to a variety of computational genomics problems, there remains a large gap in our understanding of how they build representations of regulatory genomic sequences. Here we perform systematic experiments on synthetic sequences to reveal how CNN architecture, specifically convolutional filter size and max-pooling, influences the extent that sequence motif representations are learned by first layer filters. We find that CNNs designed to foster hierarchical representation learning of sequence motifs-assembling partial features into whole features in deeper layers-tend to learn distributed representations, i.e. partial motifs. On the other hand, CNNs that are designed to limit the ability to hierarchically build sequence motif representations in deeper layers tend to learn more interpretable localist representations, i.e. whole motifs. We then validate that this representation learning principle established from synthetic sequences generalizes to in vivo sequences

The relationship between galaxy and dark matter halo size from z ∼ 3 to the present

We explore empirical constraints on the statistical relationship between the radial size of galaxies and the radius of their host dark matter haloes from z similar to 0.1-3 using the Galaxy And Mass Assembly (GAMA) and Cosmic Assembly Near Infrared Deep Extragalactic Legacy Survey (CANDELS) surveys. We map dark matter halo mass to galaxy stellar mass using relationships from abundance matching, applied to the Bolshoi-Planck dissipationless N-body simulation. We define SRHR equivalent to r(e)/R-h as the ratio of galaxy radius to halo virial radius, and SRHR lambda equivalent to r(e)/(lambda R-h) as the ratio of galaxy radius to halo spin parameter times halo radius. At z similar to 0.1, we find an average value of SRHR similar or equal to 0.018 and SRHR. similar or equal to 0.5 with very little dependence on stellar mass. Stellar radius-halo radius (SRHR) and SRHR lambda have a weak dependence on cosmic time since z similar to 3. SRHR shows a mild decrease over cosmic time for low-mass galaxies, but increases slightly or does not evolve formoremassive galaxies. We find hints that at high redshift (z similar to 2-3), SRHR. is lower for more massive galaxies, while it shows no significant dependence on stellar mass at z less than or similar to 0.5. We find that for both the GAMA and CANDELS samples, at all redshifts from z similar to 0.1-3, the observed conditional size distribution in stellar mass bins is remarkably similar to the conditional distribution of lambda R-h. We discuss the physical interpretation and implications of these results