Robust and Communication-Efficient Collaborative Learning

We consider a decentralized learning problem, where a set of computing nodes aim at solving a non-convex optimization problem collaboratively. It is well-known that decentralized optimization schemes face two major system bottlenecks: stragglers' delay and communication overhead. In this paper, we tackle these bottlenecks by proposing a novel decentralized and gradient-based optimization algorithm named as QuanTimed-DSGD. Our algorithm stands on two main ideas: (i) we impose a deadline on the local gradient computations of each node at each iteration of the algorithm, and (ii) the nodes exchange quantized versions of their local models. The first idea robustifies to straggling nodes and the second alleviates communication efficiency. The key technical contribution of our work is to prove that with non-vanishing noises for quantization and stochastic gradients, the proposed method exactly converges to the global optimal for convex loss functions, and finds a first-order stationary point in non-convex scenarios. Our numerical evaluations of the QuanTimed-DSGD on training benchmark datasets, MNIST and CIFAR-10, demonstrate speedups of up to 3x in run-time, compared to state-of-the-art decentralized optimization methods

NEW UPPER BOUND ON THE LARGEST LAPLACIAN EIGENVALUE OF GRAPHS

Let G = (V;E) be a simple, undirected graph with maximum and minimum degree ∆ and respectively, and let A be the adjacency matrix and Q be the Laplacianmatrix of G. In the past decades, the Laplacian spectrum has received much more and more attention, since it has been applied to several elds, such as randomized algorithms, combinatorial optimization problems and machine learning. In this paper, we compute lower and upper bounds for the largest Laplacian eigenvalue which is related with a given maximum and minimum degree and a given number of vertices and edges. We also compare our results in this paper with some known results

Alert Correlation through a Multi Components Architecture

Alert correlation is a process that analyzes the raw alerts produced by one or more intrusion detection systems, reduces nonrelevant ones, groups together alerts based on similarity and causality relationships between them and finally makes aconcise and meaningful view of occurring or attempted intrusions. Unfortunately, most correlation approaches use just a few components that aim only specific correlation issues and so cause reduction in correlation rate. This paper uses a general correlation model that has already been presented in [9] and is consisted of a comprehensive set of components. Then some changes are applied in the component that is related to multi-step attack scenario to detect them better and so to improve semantic level of alerts. The results of experiments with DARPA 2000 data set obviously show the effectiveness of the proposed approach.DOI:http://dx.doi.org/10.11591/ijece.v3i4.277

Nonlinear regression in tax evasion with uncertainty: a variational approach

One of the major problems in today's economy is the phenomenon of tax evasion. The linear regression method is a solution to find a formula to investigate the effect of each variable in the final tax evasion rate. Since the tax evasion data in this study has a great degree of uncertainty and the relationship between variables is nonlinear, Bayesian method is used to address the uncertainty along with 6 nonlinear basis functions to tackle the nonlinearity problem. Furthermore, variational method is applied on Bayesian linear regression in tax evasion data to approximate the model evidence in Bayesian method. The dataset is collected from tax evasion in Malaysia in period from 1963 to 2013 with 8 input variables. Results from variational method are compared with Maximum Likelihood Estimation technique on Bayeisan linear regression and variational method provides more accurate prediction. This study suggests that, in order to reduce the tax evasion, Malaysian government should decrease direct tax and taxpayer income and increase indirect tax and government regulation variables by 5% in the small amount of changes (10%-30%) and reduce direct tax and income on taxpayer and increment indirect tax and government regulation variables by 90% in the large amount of changes (70%-90%) with respect to the current situation to reduce the final tax evasion rate

Molecular diagnosis of Mycoplasma spp. Arthritis by PCR

Background: Arthritis is one of the most common inflammatory diseases worldwide. It is characterized by symptoms such as systemic inflammation and autoantibody production. The molecular mechanisms in pathogenesis of arthritis are not fully understood. Studies show that many microorganisms, including Mycoplasmas, play a role in arthritis. The PCR method is a fast and accurate molecular method for the detection of Mycoplasma genus. The main objective of this study is the detection of Mycoplasma spp arthritis by PCR method.Methods: In this study, 70 samples of synovial fluid collected from Shariati hospital. DNA samples were extracted by phenol-chloroform standard method. Using several Mycoplasma standard strains and 16S rRNA gene target optimized PCR test of Mycoplasma spp. Sensitivity and specificity test were performed on the basis of standard methods and then performed on the DNA extracted of samples.Results: PCR product was amplified by 272 bp and was observed on 2% gel electrophoresis. Specificity test with DNA of other microorganisms showed 100% specificity of these primers. The limit of detection was evaluated 100 copy/reaction. From 70 samples of synovial fluid, 2 samples (3%) were positive and 68 cases (97%) were negative.Conclusion: This study showed that a number of infectious arthritis are Mycoplasma spp at the same time, and the PCR technique can be used as a sensitive and accurate way of early detection of Mycoplasma spp arthritis. 

Designing Mu Robust Controller in Wind Turbine in Cold Weather Conditions

Due to wind turbine is in class of complex nonlinear system so the precise model of this plant is not accessible, therefore it can be categorized as an uncertain model. So, controlling of this system is a demanding topic. Many of schemes which presented for controlling of wind turbines investigate these systems in a good weather condition. However, many turbines work in severe weather condition. In this study, wind turbine is suggested in cold weather, and in ice on turbine blades which they are considered as uncertainties in the model. A robust controller is designed for the wind turbine, to control the pitch angle

The trend of marriage, childbearing, and divorce and its determinants of socioeconomic factors on divorce in Yazd province 2016-2021: A cross-sectional study

Background: In recent decades, families and their stability as an important social institution have changed significantly. Objective: This study aimed to investigate the marriage trends, childbearing, and divorce changes in Yazd province from 2016 to 2021 to estimate the effect of socioeconomic factors on divorce. Materials and Methods: A cross-sectional study was done in 2 phases. In the first phase, an ecological (time trend) was conducted to investigate the 5 yr trend in the occurrence of marriage, childbearing, and divorce, as well as the factors affecting the occurrence of divorce in the second phase. For the second phase of the study, 600 participants were selected. 300 divorced and 300 married applicants were chosen between 2016 and 2021. A binary logistic regression model was used to find the related factors affecting the occurrence of divorce. Results: The results showed a declining marriage (p = 0.05) and childbearing trend (p = 0.84), as well as an increasing trend in divorces (p = 0.02) in Yazd. Logistic regression analysis showed that college education (OR = 0.22, CI: 0.116-0.430, p < 0.001) and being self-employed (OR = 0.48, CI: 0.255-0.934, p = 0.03) could reduce the odds of divorce. In addition, nonresidents (OR = 2.1, CI: 1.314-3.562, p < 0.001), with > 10-yr age differences (OR = 3.8, CI: 1.803-8.213, p < 0.001) or the woman being older than her husband (OR = 3.4, CI: 1.981-5.848, p < 0.001) could increase the odds of divorce. Conclusion: Our results confirmed that a combination of socioeconomic characteristics affects the stability of family institutions. Key words: Family, Marriage, Childbearing, Divorce, Socioeconomic factors