Transmit design for MIMO wiretap channel with a malicious jammer

In this paper, we consider the transmit design for multi-input multi-output (MIMO) wiretap channel including a malicious jammer. We first transform the system model into the traditional three-node wiretap channel by whitening the interference at the legitimate user. Additionally, the eavesdropper channel state information (ECSI) may be fully or statistically known, even unknown to the transmitter. Hence, some strategies are proposed in terms of different levels of ECSI available to the transmitter in our paper. For the case of unknown ECSI, a target rate for the legitimate user is first specified. And then an inverse water-filling algorithm is put forward to find the optimal power allocation for each information symbol, with a stepwise search being used to adjust the spatial dimension allocated to artificial noise (AN) such that the target rate is achievable. As for the case of statistical ECSI, several simulated channels are randomly generated according to the distribution of ECSI. We show that the ergodic secrecy capacity can be approximated as the average secrecy capacity of these simulated channels. Through maximizing this average secrecy capacity, we can obtain a feasible power and spatial dimension allocation scheme by using one dimension search. Finally, numerical results reveal the effectiveness and computational efficiency of our algorithms.Comment: 2015 IEEE 81st Vehicular Technology Conference (VTC Spring

First-passage times in multi-scale random walks: the impact of movement scales on search efficiency

An efficient searcher needs to balance properly the tradeoff between the exploration of new spatial areas and the exploitation of nearby resources, an idea which is at the core of scale-free L\'evy search strategies. Here we study multi-scale random walks as an approximation to the scale- free case and derive the exact expressions for their mean-first passage times in a one-dimensional finite domain. This allows us to provide a complete analytical description of the dynamics driving the asymmetric regime, in which both nearby and faraway targets are available to the searcher. For this regime, we prove that the combination of only two movement scales can be enough to outperform both balistic and L\'evy strategies. This two-scale strategy involves an optimal discrimination between the nearby and faraway targets, which is only possible by adjusting the range of values of the two movement scales to the typical distances between encounters. So, this optimization necessarily requires some prior information (albeit crude) about targets distances or distributions. Furthermore, we found that the incorporation of additional (three, four, ...) movement scales and its adjustment to target distances does not improve further the search efficiency. This allows us to claim that optimal random search strategies in the asymmetric regime actually arise through the informed combination of only two walk scales (related to the exploitative and the explorative scale, respectively), expanding on the well-known result that optimal strategies in strictly uninformed scenarios are achieved through L\'evy paths (or, equivalently, through a hierarchical combination of multiple scales)

Search strategies of Wikipedia readers

The quest for information is one of the most common activity of human beings. Despite the the impressive progress of search engines, not to miss the needed piece of information could be still very tough, as well as to acquire specific competences and knowledge by shaping and following the proper learning paths. Indeed, the need to find sensible paths in information networks is one of the biggest challenges of our societies and, to effectively address it, it is important to investigate the strategies adopted by human users to cope with the cognitive bottleneck of finding their way in a growing sea of information. Here we focus on the case of Wikipedia and investigate a recently released dataset about users’ click on the English Wikipedia, namely the English Wikipedia Clickstream. We perform a semantically charged analysis to uncover the general patterns followed by information seekers in the multi-dimensional space of Wikipedia topics/categories. We discover the existence of well defined strategies in which users tend to start from very general, i.e., semantically broad, pages and progressively narrow down the scope of their navigation, while keeping a growing semantic coherence. This is unlike strategies associated to tasks with predefined search goals, namely the case of the Wikispeedia game. In this case users first move from the ‘particular’ to the ‘universal’ before focusing down again to the required target. The clear picture offered here represents a very important stepping stone towards a better design of information networks and recommendation strategies, as well as the construction of radically new learning paths

Multi-omics technologies applied to tuberculosis drug discovery

Multi-omics strategies are indispensable tools in the search for new anti-tuberculosis drugs. Omics methodologies, where the ensemble of a class of biological molecules are measured and evaluated together, enable drug discovery programs to answer two fundamental questions. Firstly, in a discovery biology approach, to find new targets in druggable pathways for target-based investigation, advancing from target to lead compound. Secondly, in a discovery chemistry approach, to identify the mode of action of lead compounds derived from high-throughput screens, progressing from compound to target. The advantage of multi-omics methodologies in both of these settings is that omics approaches are unsupervised and unbiased to a priori hypotheses, making omics useful tools to confirm drug action, reveal new insights into compound activity, and discover new avenues for inquiry. This review summarizes the application of Mycobacterium tuberculosis omics technologies to the early stages of tuberculosis antimicrobial drug discovery

On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D

This paper intends to understand and to improve the working principle of decomposition-based multi-objective evolutionary algorithms. We review the design of the well-established Moea/d framework to support the smooth integration of different strategies for sub-problem selection, while emphasizing the role of the population size and of the number of offspring created at each generation. By conducting a comprehensive empirical analysis on a wide range of multi-and many-objective combinatorial NK landscapes, we provide new insights into the combined effect of those parameters on the anytime performance of the underlying search process. In particular, we show that even a simple random strategy selecting sub-problems at random outperforms existing sophisticated strategies. We also study the sensitivity of such strategies with respect to the ruggedness and the objective space dimension of the target problem.Comment: European Conference on Evolutionary Computation in Combinatorial Optimization, Apr 2020, Seville, Spai