A Unifying View of Multiple Kernel Learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying general optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion's dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments

Cooperative computation offloading and resource allocation for blockchain-enabled mobile edge computing: A deep reinforcement learning approach

Mobile edge computing (MEC) is a promising paradigm to improve the quality of computation experience of mobile devices because it allows mobile devices to offload computing tasks to MEC servers, benefiting from the powerful computing resources of MEC servers. However, the existing computation-offloading works have also some open issues: 1) security and privacy issues, 2) cooperative computation offloading, and 3) dynamic optimization. To address the security and privacy issues, we employ blockchain technology that ensures the reliability and irreversibility of data in MEC systems. Meanwhile, we jointly design and optimize the performance of blockchain and MEC. In this paper, we develop a cooperative computation offloading and resource allocation framework for blockchain-enabled MEC systems. In the framework, we design a multi-objective function to maximize the computation rate of MEC systems and the transaction throughput of blockchain systems by jointly optimizing offloading decision, power allocation, block size and block interval. Due to the dynamic characteristics of the wireless fading channel and the processing queues at MEC servers, the joint optimization is formulated as a Markov decision process (MDP). To tackle the dynamics and complexity of the blockchain-enabled MEC system, we develop an A3C-based cooperation computation offloading and resource allocation algorithm to solve the MDP problem. In the algorithm, deep neural networks are optimized by utilizing asynchronous gradient descent and eliminating the correlation of data. Simulation results show that the proposed algorithm converges fast and achieves significant performance improvements over existing schemes in terms of total reward

Making graphene nanoribbons photoluminescent

We demonstrate the alignment-preserving transfer of parallel graphene nanoribbons (GNRs) onto insulating substrates. The photophysics of such samples is characterized by polarized Raman and photoluminescence (PL) spectroscopies. The Raman scattered light and the PL are polarized along the GNR axis. The Raman cross section as a function of excitation energy has distinct excitonic peaks associated with transitions between the one-dimensional parabolic subbands. We find that the PL of GNRs is intrinsically low but can be strongly enhanced by blue laser irradiation in ambient conditions or hydrogenation in ultrahigh vacuum. These functionalization routes cause the formation of sp3 defects in GNRs. We demonstrate the laser writing of luminescent patterns in GNR films for maskless lithography by the controlled generation of defects. Our findings set the stage for further exploration of the optical properties of GNRs on insulating substrates and in device geometries

The ARGO-YBJ Experiment Progresses and Future Extension

Gamma ray source detection above 30TeV is an encouraging approach for finding galactic cosmic ray origins. All sky survey for gamma ray sources using wide field of view detector is essential for population accumulation for various types of sources above 100GeV. To target the goals, the ARGO-YBJ experiment has been established. Significant progresses have been made in the experiment. A large air shower detector array in an area of 1km2 is proposed to boost the sensitivity. Hybrid detection with multi-techniques will allow a good discrimination between different types of primary particles, including photons and protons, thus enable an energy spectrum measurement for individual specie. Fluorescence light detector array will extend the spectrum measurement above 100PeV where the second knee is located. An energy scale determined by balloon experiments at 10TeV will be propagated to ultra high energy cosmic ray experiments

Precision Measurement of the Proton and Deuteron Spin Structure Functions g2 and Asymmetries A2

We have measured the spin structure functions g2p and g2d and the virtual photon asymmetries A2p and A2d over the kinematic range 0.02 < x < 0.8 and 0.7 < Q^2 < 20 GeV^2 by scattering 29.1 and 32.3 GeV longitudinally polarized electrons from transversely polarized NH3 and 6LiD targets. Our measured g2 approximately follows the twist-2 Wandzura-Wilczek calculation. The twist-3 reduced matrix elements d2p and d2n are less than two standard deviations from zero. The data are inconsistent with the Burkhardt-Cottingham sum rule if there is no pathological behavior as x->0. The Efremov-Leader-Teryaev integral is consistent with zero within our measured kinematic range. The absolute value of A2 is significantly smaller than the sqrt[R(1+A1)/2] limit.Comment: 12 pages, 4 figures, 2 table

High Altitude test of RPCs for the ARGO-YBJ experiment

A 50 m**2 RPC carpet was operated at the YangBaJing Cosmic Ray Laboratory (Tibet) located 4300 m a.s.l. The performance of RPCs in detecting Extensive Air Showers was studied. Efficiency and time resolution measurements at the pressure and temperature conditions typical of high mountain laboratories, are reported.Comment: 16 pages, 10 figures, submitted to Nucl. Instr. Met

Measurements of the Q2Q^2-Dependence of the Proton and Neutron Spin Structure Functions g1p and g1n

The structure functions g1p and g1n have been measured over the range 0.014 < x < 0.9 and 1 < Q2 < 40 GeV2 using deep-inelastic scattering of 48 GeV longitudinally polarized electrons from polarized protons and deuterons. We find that the Q2 dependence of g1p (g1n) at fixed x is very similar to that of the spin-averaged structure function F1p (F1n). From a NLO QCD fit to all available data we find $\Gamma_1^p - \Gamma_1^n =0.176 \pm 0.003 \pm 0.007$ at Q2=5 GeV2, in agreement with the Bjorken sum rule prediction of 0.182 \pm 0.005.Comment: 17 pages, 3 figures. Submitted to Physics Letters

Quasinormal modes of Schwarzschild black holes in four and higher dimensions

We make a thorough investigation of the asymptotic quasinormal modes of the four and five-dimensional Schwarzschild black hole for scalar, electromagnetic and gravitational perturbations. Our numerical results give full support to all the analytical predictions by Motl and Neitzke, for the leading term. We also compute the first order corrections analytically, by extending to higher dimensions, previous work of Musiri and Siopsis, and find excellent agreement with the numerical results. For generic spacetime dimension number D the first-order corrections go as $\frac{1}{n^{(D-3)/(D-2)}}$. This means that there is a more rapid convergence to the asymptotic value for the five dimensional case than for the four dimensional case, as we also show numerically.Comment: 12 pages, 5 figures, RevTeX4. v2. Typos corrected, references adde