Massively Parallel Algorithms for Distance Approximation and Spanners

Over the past decade, there has been increasing interest in distributed/parallel algorithms for processing large-scale graphs. By now, we have quite fast algorithms -- usually sublogarithmic-time and often $poly(\log\log n)$-time, or even faster -- for a number of fundamental graph problems in the massively parallel computation (MPC) model. This model is a widely-adopted theoretical abstraction of MapReduce style settings, where a number of machines communicate in an all-to-all manner to process large-scale data. Contributing to this line of work on MPC graph algorithms, we present $poly(\log k) \in poly(\log\log n)$ round MPC algorithms for computing $O(k^{1+{o(1)}})$-spanners in the strongly sublinear regime of local memory. To the best of our knowledge, these are the first sublogarithmic-time MPC algorithms for spanner construction. As primary applications of our spanners, we get two important implications, as follows: -For the MPC setting, we get an $O(\log^2\log n)$-round algorithm for $O(\log^{1+o(1)} n)$ approximation of all pairs shortest paths (APSP) in the near-linear regime of local memory. To the best of our knowledge, this is the first sublogarithmic-time MPC algorithm for distance approximations. -Our result above also extends to the Congested Clique model of distributed computing, with the same round complexity and approximation guarantee. This gives the first sub-logarithmic algorithm for approximating APSP in weighted graphs in the Congested Clique model

Una investigación de la congestión de productos no deseados en el análisis de envolvente de datos

Congestion is one of the most important subjects in Data Envelopment Analysis (DEA) which helps the Decision Maker (DM) to decide about changing the size of units. The estimation of congestion has attractive advantages from different perspectives. For example, the total cost of a partiular DMU, in which the congestion occurs, can be reduced by the decreases in inputs. On the other hand, the output of units can be increased by the recognizing and eliminating the congestion of DMUs and so, the total profit of decision making units can be increased. Hence, the management is eager to know how to recognize and eliminate the congestion of units. Most of the existing methods to estimation of the congestion in the literature consider only the desirable outputs. This study focuses on the evaluation of congestion in the presence of undesirable outputs and proposes an approach to recognize the congestion of units. The method is demonstrated on a numerical example to illustrate the validity of the proposed method.La congestión es uno de los temas más importantes en el análisis envolvente de datos (DEA) que ayuda al responsable de la toma de decisiones (DM) a decidir sobre el cambio de tamaño de las unidades. La estimación de la congestión tiene atractivas ventajas desde diferentes perspectivas. Por ejemplo, el costo total de una DMU en particular, en la que ocurre la congestión, puede reducirse mediante la disminución de los insumos. Por otro lado, la producción de unidades se puede incrementar reconociendo y eliminando la congestión de las DMU y así, se puede incrementar el beneficio total de las unidades de toma de decisiones. Por lo tanto, la gerencia está ansiosa por saber cómo reconocer y eliminar la congestión de unidades. La mayoría de los métodos existentes para estimar la congestión en la literatura consideran solo los resultados deseables. Este estudio se centra en la evaluación de la congestión en presencia de salidas indeseables y propone un enfoque para reconocer la congestión de unidades. El método se demuestra en un ejemplo numérico para ilustrar la validez del método propuesto

Una investigación de la congestión de productos no deseados en el análisis de envolvente de datos

La congestión es uno de los temas más importantes en el análisis envolvente de datos (DEA) que ayuda al responsable de la toma de decisiones (DM) a decidir sobre el cambio de tamaño de las unidades. La estimación de la congestión tiene atractivas ventajas desde diferentes perspectivas. Por ejemplo, el costo total de una DMU en particular, en la que ocurre la congestión, puede reducirse mediante la disminución de los insumos. Por otro lado, la producción de unidades se puede incrementar reconociendo y eliminando la congestión de las DMU y así, se puede incrementar el beneficio total de las unidades de toma de decisiones. Por lo tanto, la gerencia está ansiosa por saber cómo reconocer y eliminar la congestión de unidades. La mayoría de los métodos existentes para estimar la congestión en la literatura consideran solo los resultados deseables. Este estudio se centra en la evaluación de la congestión en presencia de salidas indeseables y propone un enfoque para reconocer la congestión de unidades. El método se demuestra en un ejemplo numérico para ilustrar la validez del método propuesto

Deepfake Detection of Occluded Images Using a Patch-based Approach

DeepFake involves the use of deep learning and artificial intelligence techniques to produce or change video and image contents typically generated by GANs. Moreover, it can be misused and leads to fictitious news, ethical and financial crimes, and also affects the performance of facial recognition systems. Thus, detection of real or fake images is significant specially to authenticate originality of people's images or videos. One of the most important challenges in this topic is obstruction that decreases the system precision. In this study, we present a deep learning approach using the entire face and face patches to distinguish real/fake images in the presence of obstruction with a three-path decision: first entire-face reasoning, second a decision based on the concatenation of feature vectors of face patches, and third a majority vote decision based on these features. To test our approach, new datasets including real and fake images are created. For producing fake images, StyleGAN and StyleGAN2 are trained by FFHQ images and also StarGAN and PGGAN are trained by CelebA images. The CelebA and FFHQ datasets are used as real images. The proposed approach reaches higher results in early epochs than other methods and increases the SoTA results by 0.4\%-7.9\% in the different built data-sets. Also, we have shown in experimental results that weighing the patches may improve accuracy

Electromagnetic scattering by lossy plasmonic and non-plasmonic half-spaces from vertically polarized incident waves

In this research, approximate analytical solutions for the scattered electromagnetic (EM) fields radiated by a vertical electric dipole (VED) antenna in the presence of a lossy half-space for ordinary and plasmonic media are investigated. First, an approximate analytical solution for the wave scattering above a lossy half-space with a smooth interface is proposed for frequencies below the very high frequency (VHF) band. The solution to the problem is given in terms of two-dimensional Fourier transforms, which leads to Sommerfeld-type integrals. The solution is decomposed into three terms. Two terms are expressed with hyperbolic functions and the third term is presented using the Gauss error function. A numerical evaluation of the integrals validates the accuracy and efficiency of the proposed solution at various frequencies and distances from the source. Second, an approximate analytical solution of the problem with a smooth interface is proposed for frequencies below 10 GHz. The solution for the intermediate Hertz potential is decomposed into two integrals and a rigorous approximate closed-form solution in the near and far field regions is presented for each term. Then, the scattered electric field (E-field) components are calculated from the intermediate Hertz potential. A numerical evaluation of the solution for different lossy half-spaces, i.e., seawater, wet earth, dry earth and lake water, validates the accuracy of the proposed solution at various frequencies and distances from the antenna. Following this work, a new asymptotic solution for the scattered EM fields above a lossy half-space with a smooth interface for ordinary and plasmonic media is proposed using the modified saddle point method. The new formulations are applied to calculate radiation patterns of different impedance half- planes for both ordinary media (e.g., seawater, silty clay soil, silty loam soil and lake water) and plasmonic media (e.g., silver and gold). A numerical evaluation of the proposed solution at various frequencies and comparisons with two alternative state- of-the-art solutions show that the proposed solution has higher accuracy for plasmonic and non-plasmonic structures. Lastly, random roughness is added to the interface, and a solution for EM scattering over a two-dimensional random rough surface with large roughness height using the generalized functions approach is proposed. The EM field derivation incorporates an arbitrary rough surface profile with small slope, a radiation source and involves all scattering orders of the scattered E-field for high and moderate contrast media. Subsequently, the first-order scattered E-field is calculated using the Neumann series solution for transverse magnetic (TM) polarization. By considering a pulsed dipole antenna and a two-dimensional Gaussian rough surface distribution with different root mean square heights and correlation lengths, the scattered E-field along with the radar cross-section is calculated. Using the result of the method of moments (MoM) as reference, a numerical evaluation of the solution for different roughness heights and contrast media demonstrates that the proposed solution is better than those of the small perturbation method (SPM), Kirchhoff approximation (KA) and small-slope approximation (SSA)

PROCJENA UTJECAJA SADRŽAJA VLAGE U UGLJENOJ PRAŠINI NA PREDVIĐANJE INDEKSA NJEZINE EKSPLOZIVNOSTI

Exploring the mechanism of coal dust explosions is essential for the development of safety techniques to prevent coal dust explosions. An explosion index can be used to estimate explosion severity. In this study, the moisture content parameter, one of the intrinsic characteristics of coal dust, was used to estimate the explosion index. For this purpose, 32 samples of coal with different moisture content were collected from different mines in Iran and were prepared as coal rounds. The coal dust explosion process was carried out in a 2-litre closed chamber. After determining the most important and influential parameters, prediction models of the explosion index were presented using linear regression. For this purpose, 75 percent of data was randomly assigned for training and 25 percent of data was used for testing and validation. The performance of these models was assessed through the root mean square error (RMSE), the proportion of variance accounted for (VAF), the mean absolute percentage error (MAPE) and the mean absolute error (MAE). Then the results of the laboratory method were compared with the results of the regression model. The results show that there is a good correlation between the laboratory results and the predictive model obtained through linear regression analysis.Istraživanje eksplozivnosti ugljene prašine iznimno je važno kod razvoja sigurnosnih tehnika za sprječavanje eksplozije ugljene prašine. Indeks eksplozivnosti koristi se za procjenu težine (snage) eksplozije. Pri tomu je sadržaj vlage jedna od intrinističkih varijabli same prašine izravno rabljena za izračun indeksa. U istraživanju su analizirana 32 uzorka različitoga sadržaja vlage prikupljena u različitim iranskim rudnicima. Eksplozivnost prašine ispitana je u dvolitarskim zatvorenim komorama. Određeni su najvažniji, tj. najrizičniji, parametri kojima se predviđa indeks eksplozivnosti uporabom linearne regresije. Skup od 75 % podataka, slučajno odabranih, uporabljen je za uvježbavanje, a 25 % za provjeru. Kvaliteta modela ispitana je korijenom srednje kvadratne pogrješke, udjelom varijance, srednjom apsolutnom postotnom i srednjom apsolutnom pogrješkom. Uspoređeni su rezultati laboratorijskoga testiranja i oni linearne regresijske analize

Reliability of functional connectivity in resting-state functional MRI

     Functional MRI is a noninvasive method in brain imaging. Localization, classification, prediction and connectivity are the most common issues. Functional connectivity is a branch of fMRI that focuses on connectivity between voxels and ROIs. There are several methods for investigating functional connectivity such as correlation analysis. In any field, it is very important that results of any research have reliability according to the experiment. Any methods and measurement instruments need to be reliable. Without reliability, results are meaningless and our research is not trustworthy. Brain imaging can be used as a valuable tool for pre-surgical planning, so the results should be highly reproducible. Test-retest reliability can be explored using the intra-class correlation coefficient (ICC). I2C2 is an extent of ICC to verify the reliability in high-dimensional data as imaging studies. 13 subjects of test-retest resting-state fMRI are used to investigate reliability. I2C2 of four ROIs are also computed (Caudate, Cingulate, Cuneus and Precentral regions). Functional connectivity is found to have moderate reliability ranging 0.6244 to 0.6941. 95% confidence interval of I2C2 is calculated by nonparametric bootstrap in which CI of Caudate region I2C2 has the shortest length.

Prevalence of Helicobacter pylori and its cagA gene in patients with gastric cancer or peptic ulcer at an Iranian medical center

Background: Iran has a high incidence rate for gastric cancer among the Middle East countries. In addition to gastric cancer, peptic ulcer is also life-threatening; thus, investigating the prevalence of Helicobacter pylori infection and other risk factors are essential. The present study was aimed to assess the frequency of H. pylori and the cagA-positive strains in patients with gastric cancer or peptic ulcer at a teaching hospital in Qom, one of the most populated cities of Iran. Materials and Methods: The presence of H. pylori was investigated in gastric cancer and peptic ulcer biopsy specimens using the standard culture method. PCR analysis was performed to detect the presence of the cagA gene. Results: The frequency of H. pylori isolates among 86 investigated biopsies was 20 (23.2%). Likewise, the rate of H. pylori was the highest when samples were examined from patients with gastric cancer (25.8%), while it was 21.8% when obtained from peptic ulcer patients. The frequency of the cagA gene in H. pylori isolates was 9 (56.2%), as confirmed by PCR. Conclusion: Our results indicated that H. Pylori infection and its virulent strains are frequent and widely spread in Qom city. The cagA gene was present in almost half of H. pylori isolates from peptic ulcer or gastric cancer patients. Therefore, it is necessary to screen it in all cases with H. pylori infection for early detection of gastric cancer

Detection of carbapenem resistance and virulence genes among Acinetobacter baumannii isolated from hospital environments in center of Iran

Carbapenem-resistant Acinetobacter baumannii are the top urgent antibiotic resistance threat in the world. The aims of this study were the determination of carbapenem-resistant genes and virulence genes among isolates from hospital environments. In this study, A. baumannii isolated from hospital environments and evaluated its antibiotic resistance, virulence factors, and resistance genes. Of 258 samples, 58 showed growth of the target organism. Antibiotic susceptibility test results considered all the A. baumannii to be multidrug-resistant isolates with the highest resistance being 36.2% to ciprofloxacin; while the most effective antibiotics with 98.3% susceptibility was piperacillin-tazobactam. Of these 58 hospital environment isolates, 18 isolates were positive for Metallo beta-lactamase. Overall, 65% of the isolates from hospital environments had many virulence factors. PCR assays demonstrated the highest and lowest positive results in csgA and cvaC gene among hospital environment isolates. Results indicate that the determination of carbapenem-resistant genes and virulence genes among isolates from hospital environments is very important