Machine Learning and Location Verification in Vehicular Networks

Location information will play a very important role in emerging wireless networks such as Intelligent Transportation Systems, 5G, and the Internet of Things. However, wrong location information can result in poor network outcomes. It is therefore critical to verify all location information before further utilization in any network operation. In recent years, a number of information-theoretic Location Verification Systems (LVSs) have been formulated in attempts to optimally verify the location information supplied by network users. Such LVSs, however, are somewhat limited since they rely on knowledge of a number of channel parameters for their operation. To overcome such limitations, in this work we introduce a Machine Learning based LVS (ML-LVS). This new form of LVS can adapt itself to changing environments without knowing the channel parameters. Here, for the first time, we use real-world data to show how our ML-LVS can outperform information-theoretic LVSs. We demonstrate this improved performance within the context of vehicular networks using Received Signal Strength (RSS) measurements at multiple verifying base stations. We also demonstrate the validity of the ML-LVS even in scenarios where a sophisticated adversary optimizes her attack location.Comment: 5 pages, 3 figure

When Does Learning in Games Generate Convergence to Nash Equilibria? The Role of Supermodularity in an Experimental Setting

This study clarifies the conditions under which learning in games produces convergence to Nash equilibria in practice. Previous work has identified theoretical conditions under which various stylized learning processes achieve convergence. One technical condition is supermodularity, which is closely related to the more familiar concept of strategic complementarities. We experimentally investigate the role of supermodularity in achieving convergence through learning. Using a game from the literature on solutions to externalities, we systematically vary a free parameter below, close to, at and beyond the threshold of supermodularity to assess its effects on convergence. We find that supermodular and ¡°near-supermodular¡± games converge significantly better than those far below the threshold. From a little below the threshold to the threshold, the improvement is statistically insignificant. Within the class of supermodular games, increasing the parameter far beyond the threshold does not significantly improve convergence. Simulation shows that while most experimental results persist in the long run, some become more pronounced.learning, supermodular games

Twist operators in higher dimensions

We study twist operators in higher dimensional CFT's. In particular, we express their conformal dimension in terms of the energy density for the CFT in a particular thermal ensemble. We construct an expansion of the conformal dimension in power series around n=1, with n being replica parameter. We show that the coefficients in this expansion are determined by higher point correlations of the energy-momentum tensor. In particular, the first and second terms, i.e. the first and second derivatives of the scaling dimension, have a simple universal form. We test these results using holography and free field theory computations, finding agreement in both cases. We also consider the `operator product expansion' of spherical twist operators and finally, we examine the behaviour of correlators of twist operators with other operators in the limit n ->1.Comment: 44 pages, 2 figure

Efficient Synthesis of Narrowly Dispersed Brush Polymers via Living Ring-Opening Metathesis Polymerization of Macromonomers

Various macromonomers (MMs) were efficiently synthesized through the copper-catalyzed “click” coupling of a norbornene moiety to the chain end of poly(methylacrylate), poly(t-butylacrylate), and polystyrene that were prepared using atom transfer radical polymerization. Ring-opening metathesis polymerization (ROMP) of these MMs was carried out using the highly active, fast-initiating ruthenium catalyst (H_2IMes)(pyr)_2(Cl)_2RuCHPh in THF at room temperature. ROMP of MMs was found to be living with almost quantitative conversions (>90%) of MMs, producing brush polymers with very low polydispersity indices of 1.01−1.07 and high Mn’s of 200−2600 kDa. The efficient ROMP of such MMs provides facile access to a variety of brush polymers and overcomes previous difficulties in the controlled polymerization of MMs. Atomic force microscopy of the brush polymer products revealed extended, wormlike shapes as a result of significant steric repulsion of densely grafted side chains

Multi-Objective Optimization for Power Efficient Full-Duplex Wireless Communication Systems

In this paper, we investigate power efficient resource allocation algorithm design for multiuser wireless communication systems employing a full-duplex (FD) radio base station for serving multiple half-duplex (HD) downlink and uplink users simultaneously. We propose a multi-objective optimization framework for achieving two conflicting yet desirable system design objectives, i.e., total downlink transmit power minimization and total uplink transmit power minimization, while guaranteeing the quality-of-service of all users. To this end, the weighted Tchebycheff method is adopted to formulate a multi-objective optimization problem (MOOP). Although the considered MOOP is non-convex, we solve it optimally by semidefinite programming relaxation. Simulation results not only unveil the trade-off between the total downlink and the total uplink transmit power, but also confirm that the proposed FD system provides substantial power savings over traditional HD systems.Comment: Accepted for presentation at the IEEE Globecom 2015, San Diego, CA, USA, Dec. 201

Application of machine learning to short-term equity return prediction

Cooper showed how a filter method could be used to predict equity returns for the next week by using information about returns and volume for the two previous weeks. Cooper's method may be regarded as a crude method of Machine Learning. Over the last 20 years Machine Learning has been successfully applied to the modeling of large data sets, often containing a lot of noise, in many different fields. When applying the technique it is important to fit it to the specific problem under consideration. We have designed and applied to Cooper's problem a practical new method of Machine Learning, appropriate to the problem, that is based on a modification of the well-known kernel regression method. We call it the Prototype Kernel Regression method (PKR). In both the period 1978-1993 studied by Cooper, and the period 1994-2004, the PKR method leads to a clear profit improvement compared to Cooper's approach. In all of 48 different cases studied, the period pre-cost average return is larger for the PKR method than Cooper's method, on average 37% higher, and that margin would increase as costs were taken into account. Our method aims to minimize the danger of data snooping, and it could plausibly have been applied in 1994 or earlier. There may be a lesson here for proponents of the Efficient Market Hypothesis in the form that states that profitable prediction of equity returns is impossible except by chance. It is not enough for them to show that the profits from an anomaly-based trading scheme disappear after costs. The proponents should also consider what would have been plausible applications of more sophisticated Machine Learning techniques before dismissing evidence against the EMH.

Power Efficient and Secure Full-Duplex Wireless Communication Systems

In this paper, we study resource allocation for a full-duplex (FD) radio base station serving multiple half-duplex (HD) downlink and uplink users simultaneously. The considered resource allocation algorithm design is formulated as a non-convex optimization problem taking into account minimum required receive signal-to-interference-plus-noise ratios (SINRs) for downlink and uplink communication and maximum tolerable SINRs at potential eavesdroppers. The proposed optimization framework enables secure downlink and uplink communication via artificial noise generation in the downlink for interfering the potential eavesdroppers. We minimize the weighted sum of the total downlink and uplink transmit power by jointly optimizing the downlink beamformer, the artificial noise covariance matrix, and the uplink transmit power. We adopt a semidefinite programming (SDP) relaxation approach to obtain a tractable solution for the considered problem. The tightness of the SDP relaxation is revealed by examining a sufficient condition for the global optimality of the solution. Simulation results demonstrate the excellent performance achieved by the proposed scheme and the significant transmit power savings enabled optimization of the artificial noise covariance matrix.Comment: 6 pages, invited paper, IEEE Conference on Communications and Network Security (CNS) 2015 in Florence, Italy, on September 30, 201