Error Analysis of ERM Algorithm with Unbounded and Non-Identical Sampling

A standard assumption in the literature of learning theory is the samples which are drawn independently from an identical distribution with a uniform bounded output. This excludes the common case with Gaussian distribution. In this paper we extend these assumptions to a general case. To be precise, samples are drawn from a sequence of unbounded and non-identical probability distributions. By drift error analysis and Bennett inequality for the unbounded random variables, we derive a satisfactory learning rate for the ERM algorithm.

Robust flocking for non-identical second-order nonlinear multi-agent systems

This paper investigates the robust flocking problem for second-order nonlinear systems with a leader and external disturbances.In contrast with most of second-order systems in the literature,the intrinsic dynamics here are nonlinear and non-identical that depend not only on the velocity but also on the position,which is more realistic.Moreover,the interaction topology is undirected and switching.Provided that the leader’s velocity may be constant or time-varying,two distributed flocking control laws have been proposed for two cases to make the differences of the velocities between all followers and the leader approach to zero asymptotically.The proposed distributed flocking control laws are both model-independent which results in the effectiveness of the controllers to cope with the different intrinsic dynamics of the followers and the leader under some assumptions on boundedness of several states.An example is given to illustrate the validity of the theoretical results.

Self-adaptive large neighborhood search algorithm for parallel machine scheduling problems

A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.

LARGE DEVIATIONS AND MODERATE DEVIATIONS FOR SUMS OF NEGATIVELY DEPENDENT RANDOM VARIABLES

In this article, we obtain the large deviations and moderate deviations for negatively dependent （ND） and non-identically distributed random variables defined on （-∞, ＋∞）. The results show that for some non-identical random variables, precise large deviations and moderate deviations remain insensitive to negative dependence structure.

Representations for reliability functions of conditional coherent systems with INID components and ordered properties

This paper presents several useful mixture representations for the reliability function of the residual live of a coherent system with independent but non-identically distributed components. These presentations are based on order statistics, signatures and mean reliability functions. We then discuss some stochastic comparisons of residual lives between two systems based on the stochastic ordering of coefficient vectors （or components） of the two systems. These results form nice extensions of some known results for the case of independent and identically distributed components.

Federated Reinforcement Learning with Adaptive Training Times for Edge Caching

To relieve the backhaul link stress and reduce the content acquisition delay,mobile edge caching has become one of the promising approaches.In this paper,a novel federated reinforcement learning(FRL)method with adaptive training times is proposed for edge caching.Through a new federated learning process with the asynchronous model training process and synchronous global aggregation process,the proposed FRL-based edge caching algorithm mitigates the performance degradation brought by the non-identically and independently distributed(noni.i.d.)characteristics of content popularity among edge nodes.The theoretical bound of the loss function difference is analyzed in the paper,based on which the training times adaption mechanism is proposed to deal with the tradeoff between local training and global aggregation for each edge node in the federation.Numerical simulations have verified that the proposed FRL-based edge caching method outperforms other baseline methods in terms of the caching benefit,the cache hit ratio and the convergence speed.

Outage Performance of Opportunistic Amplify-and-Forward Relaying over Asymmetric Fading Environments

This letter analyzes the outage probability of opportunistic amplify-and-forward relaying over asymmetric and independent but non-identically distributed (i.n.d) fading environments. The work investigates the scenarios where cooperative nodes are located at different geographical locations. As a result, the different signals are affected by different i.n.d fading channels, one may undergo Rician fading distribution and others may undergo Rayleigh fading distribution. In this letter, a lower bound of the outage probability for various asymmetric fading environments is derived at high SNR by applying the initial value theorem. The analytical model is validated through Monte-Carlo simulation results.

Art and Society in Light of Adomo＇s Non-Identity Philosophy

This article begins with a discussion of T.W. Adorno＇s exploration of the culture industry, which he sees as characterized by deception and manipulation. The article then goes on to focus on the phenomenon of ＂false culture,＂ which has been brought about by commodity fetishism＇s expansion to the cultural field in terms of both artistic creation （production） and appraciation （consumption）. The article＇s task then turns to interpreting Adomo＇s critical reflections on art on the basis of his non-identity philosophy and the intrinsic dual dimensions （both destroying and reconciling） of ＂＇negative dialectics.＂ This allows one to make the claim that there is an inner relationship between Adorno＇s theory of art and his social thought, and that these two aspects of discussion, particularly seen in relation provide a foundation for meaningful human life.