Chance Constrained Optimization for Targeted Internet Advertising

We introduce a chance constrained optimization model for the fulfillment of guaranteed display Internet advertising campaigns. The proposed formulation for the allocation of display inventory takes into account the uncertainty of the supply of Internet viewers. We discuss and present theoretical and computational features of the model via Monte Carlo sampling and convex approximations. Theoretical upper and lower bounds are presented along with a numerical substantiation

WHY BUBBLE-BURSTING IS UNPREDICTABLE: WELFARE EFFECTS OF ANTI-BUBBLE POLICY WHEN CENTRAL BANKS MAKE MISTAKES

This paper examines the effect of bubble-bursting policy in the case where the central bank sometimes tries to deflate an asset which is not, in fact, overpriced. We consider the case of a “semi-bubble,” where some traders know that an asset is overpriced, but others do not. Unlike most previous papers on bubble policy, our framework assumes rational traders. We also assume a finite time horizon, to rule out infinite horizon type bubbles. The market’s “fulfilled expectations” equilibria are derived, and standard tools of welfare economics are applied to evaluate the effect of anti-bubble policy. Under the assumption that the announcements of the financial authority can help less informed traders to learn more about a risky asset, market equilibria are presented and compared. We show that, if sellers care relatively more about the states where the central bank makes a negative bubble-bursting announcement, an announcement policy interferes with the asset’s ability to share risks. Conversely, if sellers care relatively less about the announcement states, an announcement policy improves risk sharing. “Information leakage” plays an important role in our analysis. Because of this leakage, central bank announcements can initiate further information revelation between traders. That is, the leakage effect can reveal information that the central bank, itself, does not have. However, this information leakage may not be welfare improving. Also, this leakage effect makes it difficult to predict the effects of bubble-bursting policy. This may complicate both private investment strategies and public policy analysis.greater-fool, asset bubble, asymmetric information, information leakage, Hirshleifer effect

Secure Full-Duplex Device-to-Device Communication

This paper considers full-duplex (FD) device-to-device (D2D) communications in a downlink MISO cellular system in the presence of multiple eavesdroppers. The D2D pair communicate sharing the same frequency band allocated to the cellular users (CUs). Since the D2D users share the same frequency as the CUs, both the base station (BS) and D2D transmissions interfere each other. In addition, due to limited processing capability, D2D users are susceptible to external attacks. Our aim is to design optimal beamforming and power control mechanism to guarantee secure communication while delivering the required quality-of-service (QoS) for the D2D link. In order to improve security, artificial noise (AN) is transmitted by the BS. We design robust beamforming for secure message as well as the AN in the worst-case sense for minimizing total transmit power with imperfect channel state information (CSI) of all links available at the BS. The problem is strictly non-convex with infinitely many constraints. By discovering the hidden convexity of the problem, we derive a rank-one optimal solution for the power minimization problem.Comment: Accepted in IEEE GLOBECOM 2017, Singapore, 4-8 Dec. 201

Detection of exomoons in simulated light curves with a regularized convolutional neural network

Many moons have been detected around planets in our Solar System, but none has been detected unambiguously around any of the confirmed extrasolar planets. We test the feasibility of a supervised convolutional neural network to classify photometric transit light curves of planet-host stars and identify exomoon transits, while avoiding false positives caused by stellar variability or instrumental noise. Convolutional neural networks are known to have contributed to improving the accuracy of classification tasks. The network optimization is typically performed without studying the effect of noise on the training process. Here we design and optimize a 1D convolutional neural network to classify photometric transit light curves. We regularize the network by the total variation loss in order to remove unwanted variations in the data features. Using numerical experiments, we demonstrate the benefits of our network, which produces results comparable to or better than the standard network solutions. Most importantly, our network clearly outperforms a classical method used in exoplanet science to identify moon-like signals. Thus the proposed network is a promising approach for analyzing real transit light curves in the future