The Power of Online Learning in Stochastic Network Optimization

In this paper, we investigate the power of online learning in stochastic network optimization with unknown system statistics {\it a priori}. We are interested in understanding how information and learning can be efficiently incorporated into system control techniques, and what are the fundamental benefits of doing so. We propose two \emph{Online Learning-Aided Control} techniques, $\mathtt{OLAC}$ and $\mathtt{OLAC2}$, that explicitly utilize the past system information in current system control via a learning procedure called \emph{dual learning}. We prove strong performance guarantees of the proposed algorithms: $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ achieve the near-optimal $[O(\epsilon), O([\log(1/\epsilon)]^2)]$ utility-delay tradeoff and $\mathtt{OLAC2}$ possesses an $O(\epsilon^{-2/3})$ convergence time. $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ are probably the first algorithms that simultaneously possess explicit near-optimal delay guarantee and sub-linear convergence time. Simulation results also confirm the superior performance of the proposed algorithms in practice. To the best of our knowledge, our attempt is the first to explicitly incorporate online learning into stochastic network optimization and to demonstrate its power in both theory and practice

The Power of Online Learning in Stochastic Network Optimization

In this paper, we investigate the power of online learning in stochastic network optimization with unknown system statistics {\it a priori}. We are interested in understanding how information and learning can be efficiently incorporated into system control techniques, and what are the fundamental benefits of doing so. We propose two \emph{Online Learning-Aided Control} techniques, $\mathtt{OLAC}$ and $\mathtt{OLAC2}$, that explicitly utilize the past system information in current system control via a learning procedure called \emph{dual learning}. We prove strong performance guarantees of the proposed algorithms: $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ achieve the near-optimal $[O(\epsilon), O([\log(1/\epsilon)]^2)]$ utility-delay tradeoff and $\mathtt{OLAC2}$ possesses an $O(\epsilon^{-2/3})$ convergence time. $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ are probably the first algorithms that simultaneously possess explicit near-optimal delay guarantee and sub-linear convergence time. Simulation results also confirm the superior performance of the proposed algorithms in practice. To the best of our knowledge, our attempt is the first to explicitly incorporate online learning into stochastic network optimization and to demonstrate its power in both theory and practice

Secondary-electron radiation accompanying hadronic GeV-TeV gamma-rays from supernova remnants

The synchrotron radiation from secondary electrons and positrons (SEPs) generated by hadronic interactions in the shock of supernova remnant (SNR) could be a distinct evidence of cosmic ray (CR) production in SNR shocks. Here we provide a method where the observed gamma-ray flux from SNRs, created by pion decays, is directly used to derive the SEP distribution and hence the synchrotron spectrum. We apply the method to three gamma-ray bright SNRs. In the young SNR RX J1713.7-3946, if the observed GeV-TeV gamma-rays are of hadronic origin and the magnetic field in the SNR shock is $B\gtrsim 0.5$mG, the SEPs may produce a spectral bump at $10^{-5}-10^{-2}$eV, exceeding the predicted synchrotron component of the leptonic model, and a soft spectral tail at $\gtrsim 100$keV, distinct from the hard spectral slope in the leptonic model. In the middle-aged SNRs IC443 and W44, if the observed gamma-rays are of hadronic origin, the SEP synchrotron radiation with $B\sim 400 - 500 \mu$G can well account for the observed radio flux and spectral slopes, supporting the hadronic origin of gamma-rays. Future microwave to far-infrared and hard X-ray (>100keV) observations are encouraged to constraining the SEP radiation and the gamma-ray origin in SNRs.Comment: 9 pages, 5 figures and 1 table, MNRAS accepte

Guaranteeing benefits in generational pension plans

In this paper we analyze the possibilities of intergenerational risk sharing in a generational DB pension fund. Each generation is subject to discretionary investment, indexation and contribution policies, thereby losing intergenerational diversification gains. Intergenerational risk sharing is repaired by introducing contingent claims on the generational surplus or deficit. We find that in some circumstances the values of these options can be substantial

Explaining the performance of Chinese equity funds

This paper examines the determinants of Chinese equity fund performance measured by market benchmark adjusted returns and risk adjusted return (Jensen’s Alpha). The sample covers 193 equity funds from January 2006 to December 2011, including both bear (2008 and 2011) and bull (2006, 2007, 2009, and 2010) market conditions. We use fund characteristics including size, age, and expense ratio and managerial attributes including manager structure and management education to explain fund performance. We found only expense ratios significantly influence the fund performance under all market conditions. In addition the trading cost is positively associated with fund performance under the bear market. Fund age and management structure show varying impact across bull and bear market conditions. Management education is shown to be powerless in explaining fund performance in China