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

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

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

“All the time learning... three months are equal to one year” : second language learning in a target-language community

University of Technology, Sydney. Faculty of Education.Australia hosts thousands of English language learners every year and one of the reasons learners give for this is their belief that living in the target language community naturally avails them of more language learning opportunities than are available in their homelands. In fact, learners actually learn faster and more effectively compared to the limited gains in their respective countries. Believing that the target language community has a strong role in language learning, this research focuses on the factors and opportunities which enable students to develop their language skills in informal settings outside the school. Due to the vast scope of the research area, six different types of data collection methods have been used so that a wider spectrum in SLA could be investigated. These include an exploration of learner beliefs about their language learning experiences and a study of authentic social activities and linguistic engagements within those activities. The outcome of this research suggests that language learning is not first initiated “in the head”, but starts with the social activities in which learners participate and the qualities of the linguistic challenges and opportunities within these activities. The research draws on sociocultural theory (Vygotsky 1962, 1978), ecological approach to learning (van Lier 1999) and register theory (Halliday and Hasan 1985), and also on a range of research within second language acquisition studies. The study illustrates that language learning occurs in the context of activitybased communication experiences in authentic contexts, and the more the constant challenge and varied linguistic opportunities exist in the learner’s ecology, the more and better the chances to learn language. An overall approach to understanding independent language learning and a conceptual framework for examining informal language learning opportunities, have been developed. The study concludes with some implications for pedagogical practice in English language classrooms

Multislice/multidetector-row computed tomography findings of a rare coronary anomaly: the first septal perforator branch originating from the left main coronary artery

Multislice/multidetector-row computed tomography (MDCT) is now widely used for noninvasive assessment of coronary arteries, and it may sometimes reveal coronary anomalies. Detection of such anomalies may be relevant both during follow-up and for planning cardiac or coronary surgical/interventional procedures. These anomalies may be missed unless carefully sought. In this paper, we present the MDCT images of a first septal perforator branch originating from the left main coronary artery, which represents an extremely rare coronary anomaly. To the bestof our knowledge, this is the first case in the literature where MDCT images are presented

The Krylov-proportionate normalized least mean fourth approach: Formulation and performance analysis

Cataloged from PDF version of article.We propose novel adaptive filtering algorithms based on the mean-fourth error objective while providing further improvements on the convergence performance through proportionate update. We exploit the sparsity of the system in the mean-fourth error framework through the proportionate normalized least mean fourth (PNLMF) algorithm. In order to broaden the applicability of the PNLMF algorithm to dispersive (non-sparse) systems, we introduce the Krylov-proportionate normalized least mean fourth (KPNLMF) algorithm using the Krylov subspace projection technique. We propose the Krylov-proportionate normalized least mean mixed norm (KPNLMMN) algorithm combining the mean-square and mean-fourth error objectives in order to enhance the performance of the constituent filters. Additionally, we propose the stable-PNLMF and stable-KPNLMF algorithms overcoming the stability issues induced due to the usage of the mean fourth error framework. Finally, we provide a complete performance analysis, i.e., the transient and the steady-state analyses, for the proportionate update based algorithms, e.g., the PNLMF, the KPNLMF algorithms and their variants; and analyze their tracking performance in a non-stationary environment. Through the numerical examples, we demonstrate the match of the theoretical and ensemble averaged results and show the superior performance of the introduced algorithms in different scenarios. (C) 2014 Elsevier B.V. All rights reserved

Acute brucella melitensis M16 infection model in mice treated with tumor necrosis factor-alpha inhibitors

Introduction: There is limited data in the literature about brucellosis related to an intracellular pathogen and anti-tumor necrosis factor alpha (anti-TNFα) medication. The aim of this study was to evaluate acute Brucella infections in mice receiving anti-TNFα drug treatment. Methodology: Anti-TNFα drugs were injected in mice on the first and fifth days of the study, after which the mice were infected with B. melitensis M16 strain. Mice were sacrificed on the fourteenth day after infection. Bacterial loads in the liver and spleen were defined, and histopathological changes were evaluated. Results: Neither the liver nor the spleen showed an increased bacterial load in all anti-TNFα drug groups when compared to a non-treated, infected group. The most significant histopathological findings were neutrophil infiltrations in the red pulp of the spleen and apoptotic cells with hepatocellular pleomorphism in the liver. There was no significant difference among the groups in terms of previously reported histopathological findings, such as extramedullary hematopoiesis and granuloma formation. Conclusions: There were no differences in hepatic and splenic bacterial load and granuloma formation, which indicate worsening of the acute Brucella infection in mice; in other words, anti-TNFα treatment did not exacerbate the acute Brucella spp. infection in mice. © 2015 Kutlu et al

Putting Security on the Table: The Digitalisation of Security Tabletop Games and its Challenging Aftertaste

IT-Security Tabletop Games for developers have been available in analog format; with the COVID-19 pandemic, interest in collaborative remote security games has increased. In this paper, we propose a methodology to evaluate the impact of a (remote) security game-based intervention on developers. The study design consists of the respective intervention, three questionnaires, and a small open interview guide for a focus group. A validated self-efficacy scale is used as a proxy for measuring effects on participants' ability to develop secure software. We tested this design with 9 participants (expert and novice developers and security experts) as part of a small feasibility study to understand the challenges and limitations of remote tabletop games. We describe how we selected and digitalised three security tabletop games, and report the qualitative findings from our evaluation. Setting up and running the virtual tabletop games turned out to be more challenging and complex for both moderator and participants than we expected. Completing the games required patience and persistence, and social interaction was limited. Our findings can be helpful in building and evaluating a better, more comprehensive, technically sound and issue-specific game-based training measure for developers. The methodology can be used by researchers to evaluate existing and new game designs