SELF-DEPENDENT LOCALITY PRESERVING PROJECTION WITH TRANSFORMED SPACE-ORIENTED NEIGHBORHOOD GRAPH

Locality preserving projection （LPP） is a typical and popular dimensionality reduction （DR） method,and it can potentially find discriminative projection directions by preserving the local geometric structure in data. However,LPP is based on the neighborhood graph artificially constructed from the original data,and the performance of LPP relies on how well the nearest neighbor criterion work in the original space. To address this issue,a novel DR algorithm,called the self-dependent LPP （sdLPP） is proposed. And it is based on the fact that the nearest neighbor criterion usually achieves better performance in LPP transformed space than that in the original space. Firstly,LPP is performed based on the typical neighborhood graph; then,a new neighborhood graph is constructed in LPP transformed space and repeats LPP. Furthermore,a new criterion,called the improved Laplacian score,is developed as an empirical reference for the discriminative power and the iterative termination. Finally,the feasibility and the effectiveness of the method are verified by several publicly available UCI and face data sets with promising results.

A random adaptive method to adjust MAC parameters in IEEE802.11e WLAN

The IEEE 802.11e standard is proposed to provide QoS support in WLAN by providing prioritized differentiation of traffic. Since all the stations in the same priority access category (AC) have the same set of parameters, when the number of stations increases, the probability of different stations in the same AC choosing the same values will increase, which will result in collisions. Random adaptive MAC (medium access control) parameters scheme (RAMPS) is proposed, which uses random adaptive MAC differentiation parameters instead of the static ones used in the 802.11e standard. The performance of RAMPS is compared with that of enhanced distributed coordination access (EDCA) using NS2. The results show that RAMPS can reduce collision rate of the AC and improve the throughput by using adaptive random contention window size and inter-frame spacing values. RAMPS ensures that at any given time, several flows of the same priority have different MAC parameter values. By using the random offset for the inter-frame spacing value and the backoff time, RAMPS can provide intra-AC differentiation. The simulation results show that RAMPS outperforms EDCA in terms of both throughput and end-to-end delay irrespective of the traffic load.

Probabilistic Loss Differentiation for IEEE 802.11 Wireless Networks

We start this paper by answering the questions： What requirements should a good Loss Differentiation Mechanism （LD） for 802.11 networks in loT meet？ Do the existing LDs work well in 802.11 networks when moving towards loT？ Then we present the four properties that a LD used in loT should own and the two-folded factors that we should consider when designing such a LD. Thereby, a novel LD is proposed utilizing the back- off frozen event to reveal collision probability. Our mechanism works efficiently with standard 802.11 ; only practical statistics information is needed. In ad- dition, our mechanism can be done solely by the sender without introducing extra signaling overhead. Extensive simulations show that our mechanism can be applicable to different scenarios in 802. 11 WLANs.

智力游戏

陈晨参加一种智力游戏，这种游戏可以玩无数局。每局胜则得7分，负则得0分，平则得6分。那么，不管参与多少局，不能取得的分数有多少个？

Global Exponential Stability of Fuzzy Cellular Neural Networks with Impulses and Infinite Delays

In this paper, the global exponential stability of fuzzy cellular neural networks with impulses and infinite delays is investigated. Based on an impulsive delayed integro-differential inequality and the properties of fuzzy logic operation and M-matrix, an easily verified sufficient condition is obtained. Moreover, the exponential convergent rate for the fuzzy cellular neural networks with impulses and infinite delays is also given. An example is given to illustrate the effectiveness of our theoretical result.

Nonsmooth Semi-Infinite Minmax Programming Involving Generalized(Φ, ρ)-Invexity

This paper introduces some new generalizations of the concept of （~, p）-invexity for non- differentiable locally Lipschitz functions using the tools of Clarke subdifferential. These functions are used to derive the necessary and sufficient optimality conditions for a class of nonsmooth semi-infinite minmax programming problems, where set of restrictions are indexed in a compact set. Utilizing the sufficient optimality conditions, the authors formulate three types of dual models and establish weak and strong duality results. The results of the paper extend and unify naturally some earlier results from the literature.

Analysis of epidemic spreading with feedback mechanism in weighted networks

In this paper, we investigate a new model with a generalized feedback mechanism in weighted networks. Compare to previous models, we consider the initiative response of people and the important impact of nodes with different edges on transmission rate as epidemics prevail. Furthermore, by constructing Lyapunov function, we prove that the disease-free equilibrium E^0 is globally asymptotically stable as the epidemic threshold R^＊〈 1. When R^＊ 〉 1, we obtain the permanence of epidemic and the local stability of endemic equilibrium E＊. Finally, one can find a good agreement between numerical simulations and our analytical results.