takes advantage of the fact that the expected number of support vectors is small (! 3; 000). Therefore, the existing algorithms cannot deal with more than a few thousand support vectors. In this paper we present a decomposition algorithm that is guaranteed to solve the QP problem and that does not make

Abstract. Let f(x) be a monic polynomial in Z[x] with no rational roots but with roots in Qp for all p, or equivalently, with roots mod n for all n. It is known that f(x) cannot be irreducible but can be a product of two or more irreducible polynomials, and that if f(x) is a product of m> 1

compounds directly at the site of injury or disease and providing prolonged delivery, but it also allows for the delivery of hydrophobic compounds which cannot be easily delivered via conventional methods. The stent platform The QuaDDS-QP2 stent is based on the uncoated QueST stent platform (formerly Quanam

the appropriate ontology and the structural nesting of systems. Second, we provide a generalized modeling language which enables the compositional modeling approach to be applied to either quantitative or qualitative models. We show that model composition cannot a priori determine that a quantitative model

Abstract — There is a need for high speed, low cost and low energy solutions for convex quadratic programming to enable model predictive control (MPC) to be implemented in a wider set of applications than is currently possible. For most quadratic programming (QP) solvers the computational

lighting cannot be improved a priori due to practical (e.g., large physical distance between camera and captured objects) or economical (e.g., long duration of night-time surveillance) reasons. Previous computer vision work has shown that content at a desired higher frame rate can be recovered (to some

to be fundamental to designing (H)IBE systems without random oracles, we aim to design a system where the necessary extra information will be hidden to a computationally bounded adversary. Thus, even though we cannot prevent the ciphertext from containing information about the recipient, we can design our system

policy, one that maximizes the expected targets detected over the given horizon. However, a globally optimal solution to the QP cannot always be found since the QP is nonconvex. To remedy this, we present two linearizations of the QP to equivalent 0-1 mixed integer linear programs (MIPs) whose optimal

policy, one that maximizes the expected targets detected over the given horizon. However, a globally optimal solution to the QP cannot always be found since the QP is nonconvex. To remedy this, we present two linearizations of the QP to equivalent 0-1 mixed integer linear programs (MIPs) whose optimal

attenuation coefficients from QP cannot be 14 ignored when Vs/VP reaches 0.45, which is not uncommon in near-surface settings. It is possible to invert QP from Rayleigh 15 wave attenuation coefficients in some geological setting, a concept that differs from the common perception that Rayleigh wave 16