Coarse-graining the dynamics of coupled oscillators

We present an equation-free computational approach to the study of the coarse-grained dynamics of {\it finite} assemblies of {\it non-identical} coupled oscillators at and near full synchronization. We use coarse-grained observables which account for the (rapidly developing) correlations between phase angles and oscillator natural frequencies. Exploiting short bursts of appropriately initialized detailed simulations, we circumvent the derivation of closures for the long-term dynamics of the assembly statistics.Comment: accepted for publication in Phys. Rev. Let

Self-assembly routes towards creating superconducting and magnetic arrays

Using self-assembly from colloidal suspensions of polystyrene latex spheres we prepared well-ordered templates. By electrochemical deposition of magnetic and superconducting metals in the pores of such templates highly ordered magnetic and superconducting anti-dot nano-structures with 3D architectures were created. Further developments of this template preparation method allow us to obtain dot arrays and even more complicated structures. In magnetic anti-dot arrays we observe a large increase in coercive field produced by nanoscale (50–1000nm) holes. We also find the coercive field to demonstrate an oscillatory dependence on film thickness. In magnetic dot arrays we have explored the genesis of 3D magnetic vortices and determined the critical dot size. Superconducting Pb anti-dot arrays show pronounced Little-Parks oscillations in Tc and matching effects in magnetization and magnetic susceptibility. The spherical shape of the holes results in significantly reduced pinning strength as compared to standard lithographic samples. Our results demonstrate that self-assembly template methods are emerging as a viable, low cost route to prepare sub-micron structures

A network approach for managing and processing big cancer data in clouds

Translational cancer research requires integrative analysis of multiple levels of big cancer data to identify and treat cancer. In order to address the issues that data is decentralised, growing and continually being updated, and the content living or archiving on different information sources partially overlaps creating redundancies as well as contradictions and inconsistencies, we develop a data network model and technology for constructing and managing big cancer data. To support our data network approach for data process and analysis, we employ a semantic content network approach and adopt the CELAR cloud platform. The prototype implementation shows that the CELAR cloud can satisfy the on-demanding needs of various data resources for management and process of big cancer data

Shape-induced anisotropy in antidot arrays from self-assembled templates

Using self-assembly of polystyrene spheres, well-ordered templates have been prepared on glass and silicon substrates. Strong guiding of self-assembly is obtained on photolithographically structured silicon substrates. Magnetic antidot arrays with three-dimensional architecture have been prepared by electrodeposition in the pores of these templates. The shape anisotropy demonstrates a crucial impact on magnetization reversal processes

Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications

This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional constraint propagation on single-source data, our approach is also extended to more challenging constraint propagation on multi-source data where each pairwise constraint is defined over a pair of data points from different sources. This multi-source constraint propagation has an important application to cross-modal multimedia retrieval. Extensive results have shown the superior performance of our approach.Comment: The short version of this paper appears as oral paper in ECCV 201

Sensitivity analysis and variance reduction in a stochastic NDT problem

In this paper, we present a framework to deal with uncertainty quantification in case where the ranges of variability of the random parameters are ill-known. Namely the physical properties of the corrosion product (magnetite) which frequently clogs the tube support plate of steam generator, which is inaccessible in nuclear power plants. The methodology is based on Polynomial Chaos (PC) for the direct approach and on Bayesian inference for the inverse approach. The direct Non-Intrusive Spectral Projection (NISP) method is first employed by considering prior probability densities and therefore constructing a PC surrogate model of the large-scale NDT finite element model. To face the prohibitive computational cost underlying the high dimensional random space, an adaptive sparse grid technique is applied on NISP resulting in drastic time reduction. The PC surrogate model, with reduced dimensionality, is used as a forward model in the Bayesian procedure. The posterior probability densities are then identified by inferring from few noisy experimental data. We demonstrate effectiveness of the approach by identifying the most influential parameter in the clogging detection as well as a variability range reduction

Inverse Problems in a Bayesian Setting

In a Bayesian setting, inverse problems and uncertainty quantification (UQ) --- the propagation of uncertainty through a computational (forward) model --- are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.Comment: arXiv admin note: substantial text overlap with arXiv:1312.504

The Role and Regulation of p21 in Myelopoiesis

Elevated levels of the molecular adaptor protein p21waf1/cip1 (p21) and of the IL-3 receptor alpha chain are correlated with chemoresistance and poor prognosis in acute myeloid leukemia (AML). p21 is a core regulator of many biological functions including cell cycle control, apoptosis and differentiation. Our laboratory has demonstrated a decrease in p21 expression levels during cytokine-induced granulocytic differentiation, leading us to hypothesize that p21 antagonizes granulopoiesis. The proliferative cytokine IL-3 has been shown to prevent granulocytic differentiation of murine and human myeloid progenitor cells. We also hypothesized that IL-3 inhibition of differentiation is mediated in part by p21, and tested this in murine 32Dcl3 myeloblasts that are used to model granulopoiesis. Our findings demonstrated that p21 antagonized differentiation by promoting apoptosis of cells exposed to the differentiation inducer G-CSF. We also showed that p21 prevented premature expression of primary granule proteins and contributed to maintenance of the myeloblast phenotype. Furthermore, p21 knockdown accelerated morphologic differentiation of 32Dcl3 cells stimulated to differentiate with G-CSF. We then determined how IL-3 maintains p21 expression in myeloblast cells. We showed that IL-3 stabilized p21 mRNA in myeloblasts leading to high levels of p21 protein. This effect mapped to the 3' untranslated region (UTR) of the p21 transcript. p21 transcript stabilization by IL-3 was independent of PI3-kinase and ERK pathway signaling. In vitro binding assays provided evidence that distinct sets of RNA:protein interactions occur within the proximal 303 nucleotides of the p21 3' UTR and are regulated by IL-3 and G-CSF signaling. Association of a 60-65 kDa protein with p21 riboprobes correlated with IL-3 mediated p21 mRNA stabilization, whereas binding by a 40-42 kDa protein was associated with destabilization of p21 transcripts in 32Dcl3 cells undergoing G-CSF-induced differentiation. These findings provide the first evidence for IL-3-mediated stabilization of mRNA transcripts in myeloid progenitor cells. The finding that p21 antagonized granulopoiesis is also novel. Because high levels of the IL-3 receptor and high p21 expression have separately been linked to poor outcomes in AML, IL-3 mediated p21 mRNA stabilization may contribute to differentiation blockade during AML pathogenesis