Resource-aware event triggered distributed estimation over adaptive networks

We propose a novel algorithm for distributed processing applications constrained by the available communication resources using diffusion strategies that achieves up to a 103 fold reduction in the communication load over the network, while delivering a comparable performance with respect to the state of the art. After computation of local estimates, the information is diffused among the processing elements (or nodes) non-uniformly in time by conditioning the information transfer on level-crossings of the diffused parameter, resulting in a greatly reduced communication requirement. We provide the mean and mean-square stability analyses of our algorithms, and illustrate the gain in communication efficiency compared to other reduced-communication distributed estimation schemes. © 2017 Elsevier Inc

Computationally highly efficient mixture of adaptive filters

We introduce a new combination approach for the mixture of adaptive filters based on the set-membership filtering (SMF) framework. We perform SMF to combine the outputs of several parallel running adaptive algorithms and propose unconstrained, affinely constrained and convexly constrained combination weight configurations. Here, we achieve better trade-off in terms of the transient and steady-state convergence performance while providing significant computational reduction. Hence, through the introduced approaches, we can greatly enhance the convergence performance of the constituent filters with a slight increase in the computational load. In this sense, our approaches are suitable for big data applications where the data should be processed in streams with highly efficient algorithms. In the numerical examples, we demonstrate the superior performance of the proposed approaches over the state of the art using the well-known datasets in the machine learning literature. © 2016, Springer-Verlag London

Adaptive hierarchical space partitioning for online classification

We propose an online algorithm for supervised learning with strong performance guarantees under the empirical zero-one loss. The proposed method adaptively partitions the feature space in a hierarchical manner and generates a powerful finite combination of basic models. This provides algorithm to obtain a strong classification method which enables it to create a linear piecewise classifier model that can work well under highly non-linear complex data. The introduced algorithm also have scalable computational complexity that scales linearly with dimension of the feature space, depth of the partitioning and number of processed data. Through experiments we show that the introduced algorithm outperforms the state-of-the-art ensemble techniques over various well-known machine learning data sets. © 2016 IEEE

Long-term outcome of LRBA deficiency in 76 patients after various treatment modalities as evaluated by the immune deficiency and dysregulation activity (IDDA) score

Background: Recent findings strongly support hematopoietic stem cell transplantation (HSCT) in patients with severe presentation of LPS-responsive beige-like anchor protein (LRBA) deficiency, but long-term follow-up and survival data beyond previous patient reports or meta-reviews are scarce for those patients who do not receive a transplant.Objective: This international retrospective study was conducted to elucidate the longitudinal clinical course of patients with LRBA deficiency who do and do not receive a transplant.Method: We assessed disease burden and treatment responses with a specially developed immune deficiency and dysregulation activity score, reflecting the sum and severity of organ involvement and infections, days of hospitalization, supportive care requirements, and performance indices.Results: Of 76 patients with LRBA deficiency from 29 centers (median follow-up, 10 years; range, 1-52), 24 underwent HSCT from 2005 to 2019. The overall survival rate after HSCT (median follow-up, 20 months) was 70.8% (17 of 24 patients); all deaths were due to nonspecific, early, transplant-related mortality. Currently, 82.7% of patients who did not receive a transplant (43 of 52; age range, 3-69 years) are alive. Of 17 HSCT survivors, 7 are in complete remission and 5 are in good partial remission without treatment (together, 12 of 17 [70.6%]). In contrast, only 5 of 43 patients who did not receive a transplant (11.6%) are without immunosuppression. Immune deficiency and dysregulation activity scores were significantly lower in patients who survived HSCT than in those receiving conventional treatment (P = .005) or in patients who received abatacept or sirolimus as compared with other therapies, and in patients with residual LRBA expression. Higher disease burden, longer duration before HSCT, and lung involvement were associated with poor outcome.Conclusion: The lifelong disease activity, implying a need for immunosuppression and risk of malignancy, must be weighed against the risks of HSCT.Transplantation and immunomodulatio

Mixture of set membership filters approach for big data signal processing [Btiytik Veri Sinyal Ilemesi için Ktime Uyeligi Stizgeç Birlesimi Yaklasimi]

In this work, we propose a new approach for mixture of adaptive filters based on set-membership filters (SMF) which is specifically designated for big data signal processing applications. By using this approach, we achieve significantly reduced computational load for the mixture methods with better performance in convergence rate and steady-state error with respect to conventional mixture methods. Finally, we approve these statements with the simulations done on produce data. © 2016 IEEE

Online adaptive hierarchical space partitioning classifier [Uyarlanlr Uzay B6ltimleme ile C;evrimi~i Slnlfiandlrma]

We introduce an on-line classification algorithm based on the hierarchical partitioning of the feature space which provides a powerful performance under the defined empirical loss. The algorithm adaptively partitions the feature space and at each region trains a different classifier. As a final classification result algorithm adaptively combines the outputs of these basic models which enables it to create a linear piecewise classifier model that can work well under highly non-linear complex data. The introduced algorithm also have scalable computational complexity that scales linearly with dimension of the feature space, depth of the partitioning and number of processed data. Through experiments we show that the introduced algorithm outperforms the state-of-the-art ensemble techniques over various well-known machine learning data sets. © 2016 IEEE

Effect of amino acid substitutions in the human IFN-gamma R2 on IFN-gamma responsiveness

Patients with interferon-gamma receptor (IFN-gamma R) null mutations have severe infections with poorly pathogenic Mycobacteria. The IFN-gamma R complex involves two IFN-gamma R1 and two IFN-gamma R2 chains, in which several amino acid substitutions, some linked to disease and some apparently naturally occurring, have been described. We developed a model system to study functional effects of genetic variations in IFN-gamma R2. We retrovirally transduced wild-type IFN-gamma R2 and IFN-gamma R2 carrying presently known amino acid substitutions in various human cell lines, and next determined the IFN-gamma R2 expression pattern as well as IFN-gamma responsiveness. We determined that the T58R, Q64R, E147K and K182E variants of IFN-gamma R2 are fully functional, although the Q64R variant may be expressed higher on the cell membrane. The R114C, T168N and G227R variants were identified in patients that had disseminated infections with non-tuberculous Mycobacteria. Of these genetic variants, T168N was confirmed to be completely non-functional, whereas the novel variant G227R, and the previously reported R114C, were partial functional. The impaired IFN-gamma responsiveness of R114C and G227R is mainly due to reduced receptor function, although expression on the cell membrane is reduced as well. We conclude that the T58R, Q64R, E147K and K182E variants are polymorphisms, whereas the R114C, T168N and G227R constitute mutations associated with disease. Genes and Immunity (2011) 12, 136-144; doi:10.1038/gene.2010.74; published online 20 January 2011Immunogenetics and cellular immunology of bacterial infectious disease

Effect of amino acid substitutions in the human IFN-γR2 on IFN-γ responsiveness

Patients with interferon-γ receptor (IFN-γR) null mutations have severe infections with poorly pathogenic Mycobacteria. The IFN-γR complex involves two IFN-γR1 and two IFN-γR2 chains, in which several amino acid substitutions, some linked to disease and some apparently naturally occurring, have been described. We developed a model system to study functional effects of genetic variations in IFN-γR2. We retrovirally transduced wild-type IFN-γR2 and IFN-γR2 carrying presently known amino acid substitutions in various human cell lines, and next determined the IFN-γR2 expression pattern as well as IFN-γ responsiveness. We determined that the T58R, Q64R, E147K and K182E variants of IFN-γR2 are fully functional, although the Q64R variant may be expressed higher on the cell membrane. The R114C, T168N and G227R variants were identified in patients that had disseminated infections with non-tuberculous Mycobacteria. Of these genetic variants, T168N was confirmed to be completely non-functional, whereas the novel variant G227R, and the previously reported R114C, were partial functional. The impaired IFN-γ responsiveness of R114C and G227R is mainly due to reduced receptor function, although expression on the cell membrane is reduced as well. We conclude that the T58R, Q64R, E147K and K182E variants are polymorphisms, whereas the R114C, T168N and G227R constitute mutations associated with disease