Philips

About: Philips is a company organization based out in Vantaa, Finland. It is known for research contribution in the topics: Signal & Layer (electronics). The organization has 68260 authors who have published 99663 publications receiving 1882329 citations. The organization is also known as: Koninklijke Philips Electronics N.V. & Royal Philips Electronics.

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)

Abstract: We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computations through kernel interpolation. The SKI framework clarifies how the quality of an inducing point approach depends on the number of inducing (aka interpolation) points, interpolation strategy, and GP covariance kernel. SKI also provides a mechanism to create new scalable kernel methods, through choosing different kernel interpolation strategies. Using SKI, with local cubic kernel interpolation, we introduce KISS-GP, which is 1) more scalable than inducing point alternatives, 2) naturally enables Kronecker and Toeplitz algebra for substantial additional gains in scalability, without requiring any grid data, and 3) can be used for fast and expressive kernel learning. KISS-GP costs O(n) time and storage for GP inference. We evaluate KISS-GP for kernel matrix approximation, kernel learning, and natural sound modelling.

Josephson current through a superconducting quantum point contact shorter than the coherence length

Abstract: It is shown theoretically that the critical current of a smooth and impurity-free superconducting constriction increases stepwise as a function of its width. The step height at zero temperature is e${\mathrm{\ensuremath{\Delta}}}_{0}$/\ensuremath{\Elzxh}, dependent on the energy gap ${\mathrm{\ensuremath{\Delta}}}_{0}$ of the bulk superconductor but not on the junction parameters. This is the analog of the quantized conductance of a point contact in the normal state. It is essential that the constriction is short compared to the superconducting coherence length.

Compounds and pseudo-binary alloys with the CuAl2(C16)-type structure I. Preparation and X-ray results

Abstract: A total of 46 binary compounds with the CuAl2(C16)-type structure have been prepared and their crystallographic properties (unit cell dimensions a and c and the atomic coordinate x) have been measured. Two new examples FeZr2 and GaTh2 are included. The existence of four compounds claimed to exist in this structure-type, viz., AlZr2, AlHf2, BCr2 and RuSn2, could not be confirmed. Moreover, data are presented for 11 pseudo-binary alloy systems showing complete solid-solution series, and for 12 other systems with limited regions of the CuAl2-type structure. The results show that c a is markedly dependent on the valence electron concentration, while the x-parameter more or less follows the oscillations in the c a- ratio .

Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review

Abstract: The class of geometric deformable models, also known as level sets, has brought tremendous impact to medical imagery due to its capability of topology preservation and fast shape recovery. In an effort to facilitate a clear and full understanding of these powerful state-of-the-art applied mathematical tools, the paper is an attempt to explore these geometric methods, their implementations and integration of regularizers to improve the robustness of these topologically independent propagating curves/surfaces. The paper first presents the origination of level sets, followed by the taxonomy of level sets. We then derive the fundamental equation of curve/surface evolution and zero-level curves/surfaces. The paper then focuses on the first core class of level sets, known as "level sets without regularizers." This class presents five prototypes: gradient, edge, area-minimization, curvature-dependent and application driven. The next section is devoted to second core class of level sets, known as "level sets with regularizers." In this class, we present four kinds: clustering-based, Bayesian bidirectional classifier-based, shape-based and coupled constrained-based. An entire section is dedicated to optimization and quantification techniques for shape recovery when used in the level set framework. Finally, the paper concludes with 22 general merits and four demerits on level sets and the future of level sets in medical image segmentation. We present applications of level sets to complex shapes like the human cortex acquired via MRI for neurological image analysis.