Compressive Diffusion Strategies Over Distributed Networks for Reduced Communication Load

We study the compressive diffusion strategies over distributed networks based on the diffusion implementation and adaptive extraction of the information from the compressed diffusion data. We demonstrate that one can achieve a comparable performance with the full information exchange configurations, even if the diffused information is compressed into a scalar or a single bit. To this end, we provide a complete performance analysis for the compressive diffusion strategies. We analyze the transient, steady-state and tracking performance of the configurations in which the diffused data is compressed into a scalar or a single-bit. We propose a new adaptive combination method improving the convergence performance of the compressive diffusion strategies further. In the new method, we introduce one more freedom-of-dimension in the combination matrix and adapt it by using the conventional mixture approach in order to enhance the convergence performance for any possible combination rule used for the full diffusion configuration. We demonstrate that our theoretical analysis closely follow the ensemble averaged results in our simulations. We provide numerical examples showing the improved convergence performance with the new adaptive combination method.Comment: Submitted to IEEE Transactions on Signal Processin

Single Bit and Reduced Dimension Diffusion Strategies Over Distributed Networks

We introduce novel diffusion based adaptive estimation strategies for distributed networks that have significantly less communication load and achieve comparable performance to the full information exchange configurations. After local estimates of the desired data is produced in each node, a single bit of information (or a reduced dimensional data vector) is generated using certain random projections of the local estimates. This newly generated data is diffused and then used in neighboring nodes to recover the original full information. We provide the complete state-space description and the mean stability analysis of our algorithms.Comment: Submitted to the IEEE Signal Processing Letter

A Novel Family of Adaptive Filtering Algorithms Based on The Logarithmic Cost

We introduce a novel family of adaptive filtering algorithms based on a relative logarithmic cost. The new family intrinsically combines the higher and lower order measures of the error into a single continuous update based on the error amount. We introduce important members of this family of algorithms such as the least mean logarithmic square (LMLS) and least logarithmic absolute difference (LLAD) algorithms that improve the convergence performance of the conventional algorithms. However, our approach and analysis are generic such that they cover other well-known cost functions as described in the paper. The LMLS algorithm achieves comparable convergence performance with the least mean fourth (LMF) algorithm and extends the stability bound on the step size. The LLAD and least mean square (LMS) algorithms demonstrate similar convergence performance in impulse-free noise environments while the LLAD algorithm is robust against impulsive interferences and outperforms the sign algorithm (SA). We analyze the transient, steady state and tracking performance of the introduced algorithms and demonstrate the match of the theoretical analyzes and simulation results. We show the extended stability bound of the LMLS algorithm and analyze the robustness of the LLAD algorithm against impulsive interferences. Finally, we demonstrate the performance of our algorithms in different scenarios through numerical examples.Comment: Submitted to IEEE Transactions on Signal Processin

Stochastic Subgradient Algorithms for Strongly Convex Optimization over Distributed Networks

We study diffusion and consensus based optimization of a sum of unknown convex objective functions over distributed networks. The only access to these functions is through stochastic gradient oracles, each of which is only available at a different node, and a limited number of gradient oracle calls is allowed at each node. In this framework, we introduce a convex optimization algorithm based on the stochastic gradient descent (SGD) updates. Particularly, we use a carefully designed time-dependent weighted averaging of the SGD iterates, which yields a convergence rate of $O\left(\frac{N\sqrt{N}}{T}\right)$ after $T$ gradient updates for each node on a network of $N$ nodes. We then show that after $T$ gradient oracle calls, the average SGD iterate achieves a mean square deviation (MSD) of $O\left(\frac{\sqrt{N}}{T}\right)$. This rate of convergence is optimal as it matches the performance lower bound up to constant terms. Similar to the SGD algorithm, the computational complexity of the proposed algorithm also scales linearly with the dimensionality of the data. Furthermore, the communication load of the proposed method is the same as the communication load of the SGD algorithm. Thus, the proposed algorithm is highly efficient in terms of complexity and communication load. We illustrate the merits of the algorithm with respect to the state-of-art methods over benchmark real life data sets and widely studied network topologies

Illness perceptions and quality of life among tuberculosis patients in Gezira, Sudan.

Aims: This study aimed to answer the following research question: What is the level of illness perceptions and quality of life among TB patients in Gezira state?.Methods: A descriptive study design was used. Newly diagnosed smear positive TB patients registered in Gezira state in 2010 (n=425) formed the study population. The illness perceptions were measured by using Brief Illness Perceptions Questionnaire (BIPQ). Health Related Quality of Life (HRQoL) was assessed by means of the 12-item short form Health Survey questionnaire (FS-12).Results: TB patients saw TB as having minor consequences, TB not being very well controlled by treatment, and TB as lasting long as a disease; they also associated several symptoms with TB. Furthermore, the patients had relatively poor physical and mental quality of life. Identity, consequences, personal control and emotional representations were associated with poor physical quality of life while concern about illness was associated with poor mental quality of life.Conclusion: The illness perceptions of the TB patients might influence their adherence to treatment. The poor quality of life of the TB patients in the different areas of quality of life such as daily activities and work, calls for programmes to strengthen TB information, education and counselling.Key words: Tuberculosis, patients, illness perception, quality of life, Gezira, Suda

Influence of Menstrual Cycle on Maximal Aerobic Power of Young Female Adults

The purpose of this study was to determine the exercise response to various stages of the menstrual cycle in young female African adults. Fifteen volunteer, sedentary young female adults with a regular 28-day menstrual cycle and no history of premenstrual syndrome or abnormality participated in this study. A repeated measure and three counter balanced cross over order design was used in data collection. The subjects engaged in a 20-metre shuttle run test (20-MST) at the 3rd (early follicular phase), 14th (ovulation) and 26th (late luteal phase) days of their menstrual 2 cycle. Maximal aerobic endurance performance indexes (VO2 max, run time & number of exercise laps) were recorded. One way ANOVA with repeated measures was used in data analysis. 2 The result revealed no significant differences in the short maximum endurance performance (VO2 max) [F=.554, p=0.581], run laps [F=.483, p=0.622], and run time [F=.554, p=0.581]) recorded in the various phases of the menstrual cycle at

Social Media Marketing and Relationship Quality: Zain Jordan customers’ perspective

The aim of this study is to examine whether social media marketing can affect relationship quality from customers perspective. In a review of the literature, it is clear that to date the conceptual foundations of social media marketing and its affect on relationship quality has not yet been fully developed. In response, this study will try to provide a deeper academic understanding by extending the knowledge of both social media and relationship quality theory and practice. Thus, the proposed model contributes to existing literature by empirically investigating the association between its derived components — social media use, trust, satisfaction, commitment and relationship quality— applied to the Zain customers in Jordan. Importantly, this also provides managers in services dealing with young generation, relevant information and recommendations to assist in improving their social-media marketing programs. Data was collected by self administered questionnaire from random samples drawn from the population of Zain customers using Facebook. The constructs were developed by using measurement scales adopted from prior studies. The instrument in this study was evaluated for reliability and validity. Data were analyzed using SPSS. The results in this study indicate that social media have influence on customer’s relationship quality. The main recommendations of the study is maintaining the relationship with the customers permanently by enhancing three factors: trust, satisfaction and commitment, this study has several limitations and also indicates directions for further research. Keywords: Relationship Quality Social Media, Facebook, Jorda

Inclusions and Exclusions in the Narratives of War: Gulf Arabic Press Coverage of Russia-Ukraine Conflict

Domestic policies of nation-states as well as trends in media development have further consolidated the role of mainstream media in shaping social and political processes related to international conflicts. Deregulation of the media landscape in Gulf countries has seen the side-by-side existence of both government and private media. In the current Russia-Ukraine conflict, the mass media are significantly shaping citizens’ perceptions and understanding within Gulf countries. Similarly, the kind of information disseminated by the media on the conflict plays a role in shaping the behavior of social and political structures within nation-states. While the media alone do not determine government policies, they do shape the circumstances in which policy-making takes place. The media plays a substantial role in setting the agenda for national discourse, which guides policymakers in arriving at certain actions or responses. This study explores the characteristics and trends in the coverage of the Russia-Ukraine conflict among Arabic newspapers in the Gulf countries. The intention of this study is to gain insight into factors influencing the war narratives by different national newspapers in the Gulf region, and how these could shape national responses to the conflict. Furthermore, this study identifies different features of war narratives, the inclusions and exclusions of women in framing news about the conflict, and factors that shape such frames