This paper is concerned with some feasibility issues in mathematical programs with equilibrium constraints (MPECs) where additional joint constraints are present that must be satis ed by the state and design variables of the problems. We introduce su cient conditions that guarantee the feasibility of these MPECs. It turns out that these conditions also guarantee the feasibility of the quadratic...

An example of homogeneous space which has no metric but has a connection with non–vanishing curvature suggests that " spacetime " should be not defined as a pair (M, g), a manifold plus a metric, but as a pair (M, Γ) — a differentiable manifold plus a connection. The general theory of homogeneous spaces ensures the existence of a certain well–defined connection independently of the existence of...

If A• is a bounded, constructible complex of sheaves on a complex analytic space X, and f : X → C and g : X → C are complex analytic functions, then the iterated vanishing cycles φg[−1](φf [−1]A •) are important for a number of reasons. We give a formula for the stalk cohomology H∗(φg[−1]φf [−1]A •)x in terms of relative polar curves, algebra, and the normal Morse data and micro-support of A•.

Segmentation of femurs in Anterior-Posterior x-ray images is very important for fracture detection, computer-aided surgery and surgical planning. Existing methods do not perform well in segmenting bones in x-ray images due to the presence of large amount of spurious edges. This paper presents an atlas-based approach for automatic segmentation of femurs in x-ray images. A robust global alignment...

Received () Revised () Recurrent nets are in principle capable to store past inputs to produce the currently desired output. Because of this property recurrent nets are used in time series prediction and process control. Practical applications involve temporal dependencies spanning many time steps, e.g. between relevant inputs and desired outputs. In this case, however, gradient based learning ...

Reparametrization invariant theories have a vanishing Hamiltonian and enforce their dynamics through a constraint. We specifically choose the Dirac procedure of quantization before the introduction of constraints. Consequently, for field theories, and prior to the introduction of any constraints, it is argued that the original field operator representation should be ultralocal in order to remai...

We present an overview of current lines of research on learning with recurrent neural networks (RNNs). Topics covered are: understanding and unification of algorithms, theoretical foundations, new efforts to circumvent gradient vanishing, new architectures, and fusion with other learning methods and dynamical systems theory. The structuring guideline is to understand many new approaches as diff...