Adaptive minimum symbol error rate beamforming assisted receiver for quadrature amplitude modulation systems

An adaptive beamforming assisted receiver is proposed for multiple antenna aided multiuser systems that employ bandwidth efficient quadrature amplitude modulation (QAM). A novel minimum symbol error rate (MSER) design is proposed for the beamforming assisted receiver, where the system’s symbol error rate is directly optimized. Hence the MSER approach provides a significant symbol error ratio performance enhancement over the classic minimum mean square error design. A sample-by-sample adaptive algorithm, referred to as the least symbol error rate (LBER) technique, is derived for allowing the adaptive implementation of the system to arrive from its initial beamforming weight solution to MSER beamforming solution

The Kinematics of CIV in Star-Forming Galaxies at z~1.2

We present the first statistical sample of rest-frame far-UV spectra of star-forming galaxies at z~1. These spectra are unique in that they cover the high-ionization CIV{\lambda}{\lambda}1548, 1550 doublet. We also detect low-ionization features such as SiII{\lambda}1527, FeII{\lambda}1608, AlII{\lambda}1670, NiII{\lambda}{\lambda}1741, 1751 and SiII{\lambda}1808, and intermediate-ionization features from AlIII{\lambda}{\lambda}1854, 1862. Comparing the properties of absorption lines of lower- and higher- ionization states provides a window into the multi-phase nature of circumgalactic gas. Our sample is drawn from the DEEP2 survey and spans the redshift range 1.01 < z < 1.35 ( = 1.25). By isolating the interstellar CIV absorption from the stellar P-Cygni wind profile we find that 69% of the CIV profiles are blueshifted with respect to the systemic velocity. Furthermore, CIV shows a small but significant blueshift relative to FeII (offset of the best-fit linear regression -76 $\pm$ 26 km/s). At the same time, the CIV blueshift is on average comparable to that of MgII{\lambda}{\lambda}2796, 2803. At this point, in explaining the larger blueshift of CIV absorption at the ~ 3-sigma level, we cannot distinguish between the faster motion of highly-ionized gas relative to gas traced by FeII, and filling in on the red side from resonant CIV emission. We investigate how far-UV interstellar absorption kinematics correlate with other galaxy properties using stacked spectra. These stacking results show a direct link between CIV absorption and the current SFR, though we only observe small velocity differences among different ionization states tracing the outflowing ISM.Comment: 21 pages, 14 figures, ApJ, accepte

Machine Learning Classification of SDSS Transient Survey Images

We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any transient science pipeline and is currently still done by humans, but future surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate fully machine-enabled solutions. Using features trained from eigenimage analysis (principal component analysis, PCA) of single-epoch g, r and i-difference images, we can reach a completeness (recall) of 96 per cent, while only incorrectly classifying at most 18 per cent of artefacts as real objects, corresponding to a precision (purity) of 84 per cent. In general, random forests performed best, followed by the k-nearest neighbour and the SkyNet artificial neural net algorithms, compared to other methods such as na\"ive Bayes and kernel support vector machine. Our results show that PCA-based machine learning can match human success levels and can naturally be extended by including multiple epochs of data, transient colours and host galaxy information which should allow for significant further improvements, especially at low signal-to-noise.Comment: 14 pages, 8 figures. In this version extremely minor adjustments to the paper were made - e.g. Figure 5 is now easier to view in greyscal

The Limitations of Optimization from Samples

In this paper we consider the following question: can we optimize objective functions from the training data we use to learn them? We formalize this question through a novel framework we call optimization from samples (OPS). In OPS, we are given sampled values of a function drawn from some distribution and the objective is to optimize the function under some constraint. While there are interesting classes of functions that can be optimized from samples, our main result is an impossibility. We show that there are classes of functions which are statistically learnable and optimizable, but for which no reasonable approximation for optimization from samples is achievable. In particular, our main result shows that there is no constant factor approximation for maximizing coverage functions under a cardinality constraint using polynomially-many samples drawn from any distribution. We also show tight approximation guarantees for maximization under a cardinality constraint of several interesting classes of functions including unit-demand, additive, and general monotone submodular functions, as well as a constant factor approximation for monotone submodular functions with bounded curvature

Experimental implementation of high-fidelity unconventional geometric quantum gates using NMR interferometer

Following a key idea of unconventional geometric quantum computation developed earlier [Phys. Rev. Lett. 91, 197902 (2003)], here we propose a more general scheme in such an intriguing way: $\gamma_{d}=\alpha_{g}+\eta \gamma _{g}$, where $\gamma_{d}$ and $\gamma_{g}$ are respectively the dynamic and geometric phases accumulated in the quantum gate operation, with $\eta$ as a constant and $\alpha_{g}$ being dependent only on the geometric feature of the operation. More arrestingly, we demonstrate the first experiment to implement a universal set of such kind of generalized unconventional geometric quantum gates with high fidelity in an NMR system.Comment: 4 pages, 3 figure