Cache-aided full-duplex: delivery time analysis and optimization

Edge caching has received much attention as a promising technique to overcome the stringent latency and data-hungry challenges in the future generation wireless networks. Meanwhile, full-duplex (FD) transmission can potentially double the spectral efficiency by allowing a node to receive and transmit at the same frequency band simultaneously. In this paper, we investigate the delivery time performance of a cache-aided FD system, in which an edge node, operates in FD mode, serves users via wireless channels and is equipped with a cache memory. Firstly, we derive a closed-form expression for the average delivery time by taking into account the uncertainties of both backhaul and access wireless channels. The derived analysis allows the examination of the impact of key parameters, e.g., cache size and transmit power. Secondly, a power optimization problem is formulated to minimize the average delivery time. To deal with the non-convexity of the formulated problem, we propose an iterative optimization algorithm based on the bisection method. Finally, numerical results are presented to demonstrate the effectiveness of the proposed algorithm. A significant delivery time reduction is achieved by the proposed optimization compared to the FD reference and half-duplex counterpart.

     

Universality in ant behaviour

Prediction for social systems is a major challenge. Universality at the social level has inspired a unified theory for urban living but individual variation makes predicting relationships within societies difficult. Here, we show that in ant societies individual average speed is higher when event duration is longer. Expressed as a single scaling function, this relationship is universal because for any event duration an ant, on average, moves at the corresponding average speed except for a short acceleration and deceleration at the beginning and end. This establishes cause and effect within a social system and may inform engineering and control of artificial ones.     

An Autonomic QoS-centric Architecture for Integrated Heterogeneous Wireless Networks

In this paper the problem of seamless mobility and proficient joint radio resource management over an all-IP internetworked wireless heterogeneous environment is addressed. Nodes’ autonomicity is envisioned as the enabler to devise a Quality of Service (QoS) aware architecture for supporting a variety of services, founded on a common utility based framework that provides enhanced flexibility in reflecting different access networks’ type of resources and diverse QoS prerequisites, under a unified QoS-aware resource allocation optimization problem. This allows a more in-depth intrinsic wireless network convergence, beyond All-IP, driven by QoS-oriented resource management. This vision is demonstrated and instantiated for integrated WLAN and cellular (both CDMA and OFDMA) networks, providing a viable path towards the evolution and realization of the future wireless networking paradigm. Initial numerical results demonstrate the effectiveness of the proposed architecture and reveal the benefits of such a service oriented paradigm against other existing access oriented autonomic designs.     

Minimum Moment Method for Resource Leveling Using Entropy Maximization

The minimum moment method for resource leveling is revisited and restated as an entropy-maximization problem. The minimum moment method assumes that the moment of the daily resource demands about the horizontal axis of a project’s resource histogram is a good measure of the resource utilization and that the optimal resource allocation exists when the total moment is at a minimum, thus when the resource histogram is of rectangular shape. The entropy-maximization method proposed in this paper makes use of the general theory of entropy and two of its principal properties (subadditivity and maximality) to revisit the minimum moment method for resource leveling. The entropy-maximization method presented allows for activity stretching and provides resource allocation solutions that show improvement over previous approaches. A case study is also presented that validates the results.     

Community detection in Social Media

The proposed survey discusses the topic of community detection in the context of Social Media. Community detection constitutes a significant tool for the analysis of complex networks by enabling the study of mesoscopic structures that are often associated with organizational and functional characteristics of the underlying networks. Community detection has proven to be valuable in a series of domains, e.g. biology, social sciences, bibliometrics. However, despite the unprecedented scale, complexity and the dynamic nature of the networks derived from Social Media data, there has only been limited discussion of community detection in this context. More specifically, there is hardly any discussion on the performance characteristics of community detection methods as well as the exploitation of their results in the context of real-world web mining and information retrieval scenarios. To this end, this survey first frames the concept of community and the problem of community detection in the context of Social Media, and provides a compact classification of existing algorithms based on their methodological principles. The survey places special emphasis on the performance of existing methods in terms of computational complexity and memory requirements. It presents both a theoretical and an experimental comparative discussion of several popular methods. In addition, it discusses the possibility for incremental application of the methods and proposes five strategies for scaling community detection to real-world networks of huge scales. Finally, the survey deals with the interpretation and exploitation of community detection results in the context of intelligent web applications and services.     

Subclass discriminant Nonnegative Matrix Factorization for facial image analysis

Nonnegative Matrix Factorization (NMF) is among the most popular subspace methods, widely used in a variety of image processing problems. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis inspired criteria has been proposed, which achieves an efficient decomposition of the provided data to its discriminant parts, thus enhancing classification performance. However, this approach possesses certain limitations, since it assumes that the underlying data distribution is unimodal, which is often unrealistic. To remedy this limitation, we regard that data inside each class have a multimodal distribution, thus forming clusters and use criteria inspired by Clustering based Discriminant Analysis. The proposed method incorporates appropriate discriminant constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space, while taking into account subclass information. The developed algorithm has been applied for both facial expression and face recognition on three popular databases. Experimental results verified that it successfully identified discriminant facial parts, thus enhancing recognition performance.     

Picture deformation

Algebraic picture generation based on a pixel deformation theory is presented. The main tool used is the deformation monoid which simulates the algebraic structure of pictures viewed as rectangular arrays with operations the horizontal and vertical concatenation. Picture languages generated by grammatical systems are considered and a Chomsky-like normal form as well as an iteration lemma are established. Infinite pictures are obtained as the ω-completion of the set of finite pictures ordered by picture refinement. Regular fractal pictures (such as the Sierpinski Carpet, the Cantor dust, etc.) are defined as the components of the least solution of systems whose right hand side members are finite pictures. They constitute the least class of pictures containing the finite pictures and closed under substitution and the self similarity operation. Solving non deterministic picture program schemes we get the so called ∞-refinement languages which consist of finite and infinite pictures. For such languages the emptiness and finiteness problems are decidable.