The difference between memory and prediction in linear recurrent networks

Recurrent networks are trained to memorize their input better, often in the hopes that such training will increase the ability of the network to predict. We show that networks designed to memorize input can be arbitrarily bad at prediction. We also find, for several types of inputs, that one-node networks optimized for prediction are nearly at upper bounds on predictive capacity given by Wiener filters, and are roughly equivalent in performance to randomly generated five-node networks. Our results suggest that maximizing memory capacity leads to very different networks than maximizing predictive capacity, and that optimizing recurrent weights can decrease reservoir size by half an order of magnitude

Methods of optimizing X-ray optical prescriptions for wide-field applications

We are working on the development of a method for optimizing wide-field X-ray telescope mirror prescriptions, including polynomial coefficients, mirror shell relative displacements, and (assuming 4 focal plane detectors) detector placement along the optical axis and detector tilt. With our methods, we hope to reduce number of Monte-Carlo ray traces required to search the multi-dimensional design parameter space, and to lessen the complexity of finding the optimum design parameters in that space. Regarding higher order polynomial terms as small perturbations of an underlying Wolter I optic design, we begin by using the results of Monte-Carlo ray traces to devise trial analytic functions, for an individual Wolter I mirror shell, that can be used to represent the spatial resolution on an arbitrary focal surface. We then introduce a notation and tools for Monte-Carlo ray tracing of a polynomial mirror shell prescription which permits the polynomial coefficients to remain symbolic. In principle, given a set of parameters defining the underlying Wolter I optics, a single set of Monte-Carlo ray traces are then sufficient to determine the polymonial coefficients through the solution of a large set of linear equations in the symbolic coefficients. We describe the present status of this development effort.Comment: 14 pages, to be presented at SPIE conference 7732 (paper 93

Physical bounds and radiation modes for MIMO antennas

Modern antenna design for communication systems revolves around two extremes: devices, where only a small region is dedicated to antenna design, and base stations, where design space is not shared with other components. Both imply different restrictions on what performance is realizable. In this paper properties of both ends of the spectrum in terms of MIMO performance is investigated. For electrically small antennas the size restriction dominates the performance parameters. The regions dedicated to antenna design induce currents on the rest of the device. Here a method for studying fundamental bound on spectral efficiency of such configurations is presented. This bound is also studied for $N$-degree MIMO systems. For electrically large structures the number of degrees of freedom available per unit area is investigated for different shapes. Both of these are achieved by formulating a convex optimization problem for maximum spectral efficiency in the current density on the antenna. A computationally efficient solution for this problem is formulated and investigated in relation to constraining parameters, such as size and efficiency

Optimizing time and space MIMO antenna system for frequency selective fading channels

Smart or adaptive antennas promise to provide significant increases in system capacity and performance in wireless communication systems. In this paper, we investigate the use of adaptive antennas at the base and mobile stations, operating jointly, to maximize the average signal-to-interference and noise ratio (SINR) of each packet in the system for frequency selective channels with prior knowledge of the channel at the transmitter. Our approach is based on deriving an analytic formula for the average packet SINR and using the Lagrange multiplier method to determine an optimum. We derive necessary conditions for an optimum solution and propose an analytical expression for the optimum. Our analytical expression is not guaranteed to be the global optimum but it does satisfy the derived necessary conditions and, in addition for frequency flat channels, our results reduce to expressions for optimal weights previously published. To demonstrate the potential of the proposed system, we provide Monte Carlo simulation results of the system bit-error rates and make comparisons with other adaptive antenna systems. These show that significant improvements in performance are possible in a wireless communications context