Sharif Digital Repository

People use social networks to share millions of stories every day, but these stories rarely become viral. Can we estimate the probability that a story becomes a viral cascade If so, can we find a set of users that are more likely to trigger viral cascades These estimation and maximization problems are very challenging since both rare-event nature of viral cascades and efficiency requirement should be considered. Unfortunately, this problem still remains largely unexplored to date. In this paper, given temporal dynamics of a network, we first develop an efficient viral cascade probability estimation method, VICE, that leverages an special importance sampling approximation to achieve high... 

People use social networks to share millions of stories every day, but these stories rarely become viral. Can we estimate the probability that a story becomes a viral cascade? If so, can we find a set of users that are more likely to trigger viral cascades? These estimation and maximization problems are very challenging since both rare-event nature of viral cascades and efficiency requirement should be considered. Unfortunately, this problem still remains largely unexplored to date. In this paper, given temporal dynamics of a network, we first develop an efficient viral cascade probability estimation method, ViCE, that leverages an special importance sampling approximation to achieve high... 

Social media usage is one of the most popular online activities and people shares millions of message in a short time; however this information rarely goes viral. The diffusion process begins with an initial set of source nodes and continues with other nodes. In addition, the viral cascade is triggered when the number of infected nodes exceeds a specific threshold. Then, we find an initial set of source nodes that maximizes the number of infected nodes given the source nodes. This study aims to answer the following questions: how does a spread like a viral cascade propagate in a network? Do the structural properties of the propagation pattern play an important role in virality? If so, can we... 

Wireless mesh networks (WMNs) are well-promising future networks especially for ubiquitous connection to Internet. In this paper, we propose an analytical model in order to evaluate the maximum stable throughput of a WMN based on a simplified version of IEEE 802.11s MAC protocol. Our modeling approach is based on a multi-class open queueing network. In this respect, we map some of the important features of IEEE 802.11s MAC protocol, esp., its contention-based and contention-free periods onto different parameters of the proposed queueing network. By solving the related traffic equations and applying the stability condition, we determine the maximum stable throughput, i.e., the maximum packet... 

This paper presents the stability analysis for a single-phase phase-locked loop (PLL) for power systems applications. The dynamical equations of a previously presented single-phase PLL are systematically derived and linearized for design and stability analysis purposes. Based on the linearized model of the PLL, parameters of the loop filter and the PI controller are accurately determined. The main advantages of the PI controller are discussed as well as its design procedure. Moreover, it is shown that, unlike the conventional single-phase PLLs, the mentioned PLL does not suffer from double-frequency ripples. Simulations carried out in MATLAB and SIMULINK verify the performance of the PLL... 

There have been many applications for machine learning algorithms in different fields. The importance of hyperparameters for machine learning algorithms is their control over the behaviors of training algorithms and their crucial impact on the performance of machine learning models. Tuning hyperparameters crucially affects the performance of machine learning algorithms, and future advances in this area mainly depend on well-tuned hyperparameters. Nevertheless, the high computational cost involved in evaluating the algorithms in large datasets or complicated models is a significant limitation that causes inefficiency of the tuning process. Besides, increased online applications of machine... 

A heuristic approach is proposed to solve a real Periodic Vehicle Routing Problem (PVRP) that aims at extending the market share with the minimum transportation cost. In PVRP, vehicles are assigned to routes in different workdays to satisfy the frequency of visiting customers. The objective is to determine the scheduling and routing policies of the vehicles to minimise the fleet size and to maximise the selling benefits. The vehicle capacity and the trip time are the main constraints. The proposed approach is verified by real data and obtained results imply a significant improvement as compared to the current status  

In this paper, Bat Algorithm is proposed to solve the Optimal Power Flow (OPF) problem. The approach employs a Meta heuristic optimization algorithm called bat optimization algorithm to determine the optimal settings of control variables of OPF problem. The performance of proposed method examined on the IEEE 9, 14, 30 and 57-buses test systems with fuel cost minimization as the objective function. The numerical results are compared to those of Particle Swarm Optimization (PSO), Harmony Search (HS) and Cuckoo algorithms. Simulation results obtained from the proposed approach demonstrate that this method provide effective and robust high-quality answer for the OPF problem. © 2016 IEEE  

The huge availability of data in the last decade has raised the opportunity for the better use of data in decision-making processes. The idea of using the existing data to achieve a more coherent reality solution has led to a branch of optimization called data-driven optimization. On the one hand, the presence of uncertain variables in these datasets makes it crucial to design robust optimization methods in this area. On the other hand, in many real-world problems, the closed-form of the objective function is not available and a meta-model based framework is necessary. Motivated by the above points, in this paper a Gaussian process is used in a Bayesian optimization framework to design a...