Asymptotic Behavior of Error Exponents in the Wideband Regime

In this paper, we complement Verd\'{u}'s work on spectral efficiency in the wideband regime by investigating the fundamental tradeoff between rate and bandwidth when a constraint is imposed on the error exponent. Specifically, we consider both AWGN and Rayleigh-fading channels. For the AWGN channel model, the optimal values of $R_z(0)$ and $\dot{R_z}(0)$ are calculated, where $R_z(1/B)$ is the maximum rate at which information can be transmitted over a channel with bandwidth $B/2$ when the error-exponent is constrained to be greater than or equal to $z.$ Based on this calculation, we say that a sequence of input distributions is near optimal if both $R_z(0)$ and $\dot{R_z}(0)$ are achieved. We show that QPSK, a widely-used signaling scheme, is near-optimal within a large class of input distributions for the AWGN channel. Similar results are also established for a fading channel where full CSI is available at the receiver.Comment: 59 pages, 6 figure

Integrable representations of the quantum affine special linear superalgebra

The simple integrable modules with finite dimensional weight spaces are classified for the quantum affine special linear superalgebra \U_q(\hat{\mathfrak{sl}}(M|N)) at generic $q$. Any such module is shown to be a highest weight or lowest weight module with respect to one of the two natural triangular decompositions of the quantum affine superalgebra depending on whether the level of the module is zero or not. Furthermore, integrable \U_q(\hat{\mathfrak{sl}}(M|N))-modules at nonzero levels exist only if $M$ or $N$ is $1$.Comment: 31 page

Ultra High Energy Cosmic Rays: Strangelets? -- Extra dimensions, TeV-scale black holes and strange matter

The conjecture that ultra high energy cosmic rays (UHECRs) are actually strangelets is discussed. Besides the reason that strangelets can do as cosmic rays beyond the GZK-cutoff, another argument to support the conjecture is addressed in this letter via the study of formation of TeV-scale microscopic black holes when UHECRs bombarding bare strange stars. It is proposed that the exotic quark surface of a bare strange star could be an effective astro-laboratory in the investigations of the extra dimensions and of the detection of ultra-high energy neutrino fluxes. The flux of neutrinos (and other point-like particles) with energy >2.3 x 10^{20} eV could be expected to be smaller than 10^{-26} cm^{-2}$ s^{-1} if there are two extra spatial dimensions.Comment: accepted by Chin. Phys. Lett., or at http://vega.bac.pku.edu.cn/~rxxu/publications/index_P.ht

Method and apparatus for contour mapping using synthetic aperture radar

By using two SAR antennas spaced a known distance, B, and oriented at substantially the same look angle to illuminate the same target area, pixel data from the two antennas may be compared in phase to determine a difference delta phi from which a slant angle theta is determined for each pixel point from an equation Delta phi = (2 pi B/lambda)sin(theta - alpha), where lambda is the radar wavelength and alpha is the roll angle of the aircraft. The height, h, of each pixel point from the aircraft is determined from the equation h = R cos theta, and from the known altitude, a, of the aircraft above sea level, the altitude (elevation), a', of each point is determined from the difference a - h. This elevation data may be displayed with the SAR image by, for example, quantizing the elevation at increments of 100 feet starting at sea level, and color coding pixels of the same quantized elevation. The distance, d, of each pixel from the ground track of the aircraft used for the display may be determined more accurately from the equation d = R sin theta

Learning Loosely Connected Markov Random Fields

We consider the structure learning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional independence test based algorithm for learning the underlying graph structure. The novel maximization step in our algorithm ensures that the true edges are detected correctly even when there are short cycles in the graph. The number of samples required by our algorithm is C*log p, where p is the size of the graph and the constant C depends on the parameters of the model. We show that several previously studied models are examples of loosely connected Markov random fields, and our algorithm achieves the same or lower computational complexity than the previously designed algorithms for individual cases. We also get new results for more general graphical models, in particular, our algorithm learns general Ising models on the Erdos-Renyi random graph G(p, c/p) correctly with running time O(np^5).Comment: 45 pages, minor revisio