Densely integrated microroring-resonator based components for fiber-to-the-home applications

The design, realization and characterization of densely integrated optical components based on thermally tunable microring resonators fabricated in Si3N4/SiO2 is described. Current copper based networks are unable to meet future bandwidth demands and will therefore be slowly replaced with optical networks. A promising technology for these networks is WDM-PON. Currently, however, this technology is too expensive. The Broadband Photonics and NAIS projects within which the presented work was carried out both seek to lower the cost of WDM-PON implementations through dense integration of reconfigurable optical components based on optical microring resonators.\ud \ud A number of realized designs, all based on a basic resonator building block are discussed. This building block is based on a 2.0 x 0.14 µm port waveguide from where the light is coupled into a tunable ring resonator that has waveguide dimensions of 2.5 x 0.18 µm and a radius of 50 µm. Amongst the less complex realized devices is a wavelength selective optical switch based on two cascaded resonators. The switch measures only 200 µm x 200 µm. The “on/off” attenuation of the switch is 12 dB. When the switch is “on” the crosstalk with the adjacent channels is ≈-20 dB (channel spacing of 0.8 nm). \ud In addition more complex devices have been realized. The characterization of two different types of OADM, for use at 1310 nm or at 1550 nm, and a Router are discussed. The 1550 nm OADM could be fully tuned and could be configured to drop one or more channels. In addition system level measurements were performed in this OADM. A 40 Gbit/s could be dropped to a single channel without a significant penalty in BER. In addition multicasting was demonstrated. The same reconfigurability was also shown for the 1300 nm OADM. Finally the 1300 nm router is discussed and basic functionality of the router, dropping one, two or three channels to a single output is demonstrated

CRC 1114 - Report Membrane Deformation by N-BAR Proteins: Extraction of membrane geometry and protein diffusion characteristics from MD simulations

We describe simulations of Proteins and artificial pseudo-molecules interacting and shaping lipid bilayer membranes. We extract protein diffusion Parameters, membrane deformation profiles and the elastic properties of the used membrane models in preparation of calculations based on a large scale continuum model

Partial ovoids and partial spreads in symplectic and orthogonal polar spaces

We present improved lower bounds on the sizes of small maximal partial ovoids and small maximal partial spreads in the classical symplectic and orthogonal polar spaces, and improved upper bounds on the sizes of large maximal partial ovoids and large maximal partial spreads in the classical symplectic and orthogonal polar spaces. An overview of the status regarding these results is given in tables. The similar results for the hermitian classical polar spaces are presented in [J. De Beule, A. Klein, K. Metsch, L. Storme, Partial ovoids and partial spreads in hermitian polar spaces, Des. Codes Cryptogr. (in press)]

Partial ovoids and partial spreads in finite classical polar spaces

We survey the main results on ovoids and spreads, large maximal partial ovoids and large maximal partial spreads, and on small maximal partial ovoids and small maximal partial spreads in classical finite polar spaces. We also discuss the main results on the spectrum problem on maximal partial ovoids and maximal partial spreads in classical finite polar spaces

The feasibility of radiolabeling for human serum albumin (HSA) adsorption studies

Human serum albumin (HSA) was labeled in various ways and with different radioactive labels (Technetium-99m and Iodine-125). Characterization with electrophoresis on polyacryl gel and immunoelectrophoresis did not reveal differences between labeled and nonlabeled HSA. The release of the label from labeled proteins in phosphate buffer (pH 7.4) was studied as a function of time. 125I-labeled proteins were stable and 99mTc-labeled proteins showed different stabilities depending on the labeling method which was used. The adsorption behavior of labeled HSA and HSA onto polystyrene (PS) and silicon rubber (SR) was studied by using two methods. It appeared that all labeled HSA compounds showed a preferential adsorption onto PS (and SR) substrates. The 99mTc-labeled HSA showed a high value of the preferential adsorption factor (φ 1). The φ value for 125I-labeled HSA was about 1.4. It was also shown that φ was dependent on the kind of substrate used. The methods developed to determine preferential adsorption of labeled proteins compared to their nonlabeled analogs are also generally applicable for different types of compounds

Structured learning of assignment models for neuron reconstruction to minimize topological errors

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Structured learning provides a powerful framework for empirical risk minimization on the predictions of structured models. It allows end-to-end learning of model parameters to minimize an application specific loss function. This framework is particularly well suited for discrete optimization models that are used for neuron reconstruction from anisotropic electron microscopy (EM) volumes. However, current methods are still learning unary potentials by training a classifier that is agnostic about the model it is used in. We believe the reason for that lies in the difficulties of (1) finding a representative training sample, and (2) designing an application specific loss function that captures the quality of a proposed solution. In this paper, we show how to find a representative training sample from human generated ground truth, and propose a loss function that is suitable to minimize topological errors in the reconstruction. We compare different training methods on two challenging EM-datasets. Our structured learning approach shows consistently higher reconstruction accuracy than other current learning methods.Peer ReviewedPostprint (author's final draft