Sharif Digital Repository

Nowadays, there exist many real networks with distinctive features in comparison with random networks. Social networks, collaboration networks, citation networks, protein networks and communication networks are some example of complex network classes. Nowadays these networks are widespread and have many applications and the study of complex networks is an important research area. In many applications, the “synthetic networks generation” is one of the first levels of complex networks analysis. This level has many applications such as simulation and extrapolation. Many generative models are proposed for complex network modeling in recent years. By the use of these models, synthetic networks... 

In recent years, GPUs are becoming ideal candidates for processing a variety of high performance applications. By relying on thousands of concurrent threads in applications and the computational power of large numbers of computing units, GPGPUs have provided high efficiency and throughput. To achieve the potential computational power of GPGPUs in broader types of applications, we need to apply some modifications. By understanding the features and properties of applications, we can execute them in a more proper way on GPUs. Therefore, considering applications’ behavior, we define 5 different categories for them. Every category has special definitions, and we change the configuration of GPU... 

In this study, a mathematical model for health-care facility location in two level and multi-services has been described. The facilities has two levels of clinic and hospital that has inclusive hierarchy property. In clinics, only outpatient services delivered. But, in hospitals in addition to handle outpatient services, inpatient services and emergency services are provided. In this research, we practice on queuing theory in order to consider the serious uncertainties in the health service, for instance, random demand and random service time, and by the help of which the criteria for considering the service level is calculated. Then by using applicable change variable and service level... 

There are several methods for measuring thin film thickness, however, for the analysis of liquid film motors [1] we need a method which is capable of measuring the thickness using a single image of the film. In this work, we use the colors that appear on thin films, such as soup bubbles, which is a result of light interference to calculate the thickness of the layer  

One of the most elusive problems in quantum mechanics is the transition between classical and quantum physics. This problem can be traced back to the Schrodinger's cat. A key element that lies at the center of this problem is the lack of a clear understanding and characterization of macroscopic quantum states. Our understanding of Macroscopic Quantumness relies on states such as the Greenberger-Horne-Zeilinger(GHZ) or the NOON state. Here we take a first principle approach to this problem. We start from coherence as the key quantity that captures the notion of quantumness and demand the quantumness to be collective and macroscopic. To this end, we introduce macroscopic coherence which is the... 

With confidence in the recorded observations, the RLS method no longer estimates the recorded measurements by sensors, i.e. the displacement and speed of the floors, and only estimates the parameters. In contrast, in the EKF method, in addition to estimating the structure's parameters, a more precise estimation of the observations recorded by the sensors has been done by accepting the noise in the recorded observations. These methods, which are based on the Bayesian updating, investigate the two primary sources of uncertainty in a problem: a) measurement noise or observation noise, and b) process noise, which includes modeling errors. In these methodologies, the unknown system parameters,... 

Entanglement is one of the key resources of quantum information science which makes identification of entangled states essential to a wide range of quantum technologies and phenomena.This problem is however both computationally and experimentally challenging.Here we use autoencoder neural networks to find semi-optimal measurements for detection of entangled states. We show that it is possible to find high-performance entanglement detectors with as few as three measurements. Also, with the complete information of the state, we develop a neural network that can identify all two-qubits entangled states almost perfectly.This result paves the way for automatic development of efficient... 

The vortex tube is a simple device that injects compressed gas (air) tangentially into the vortex chamber through one or more injection nozzles. After entering, the flow becomes rotational and an axial cold flow goes towards the cold outlet and a peripheral hot flow goes towards the hot outlet. Besides all the different applications of the vortex tube, the main application of this device is cooling. Here, the goal is to optimize the geometry and physical conditions to improve the performance, which is done by using a commercial software and numerical analysis of a vortex tube to understand the flow physics and optimization. In this research, we use experimental and numerical data for... 

Increasing in social communication via electronic ways has been made social network analysis of these communications more important each day. One of the most important aspects in social network analysis is community detection in such networks. There are many different ways to extract communities from social graph structure which in some of them the content of communication between actors has been noticed in community extraction algorithm. In this thesis after a short survey over advantages and disadvantages of existing methods for community detection, a new method for extracting communities from social networks has been suggested which in addition to streaming property of data it spot the... 

Quantum Information Processing (QIP) is one of the active areas of research in both theoretical and experimental physics. Any experimental technique that is used for a scalable implementation of QIP must satisfy DiVincenzo’s criteria [17]. Nuclear Magnetic Resonance (NMR) satisfies many of these conditions, but it is not scalable and cannot initialize the qubits to pure state [28]. NMR can be a great platform for studying the fundamentals of QIP. In this project, for a two­qubit system, we prepare pseudo pure states from the initial mixed states by using unitary operations and implement CNOT gates. According to the results of our experiments, we can apply all the gates with high fidelity.... 

The importance of using a fast and automatic method to classify variable stars for large amounts of data is undeniable. There have been many attempts for classifying variable stars by traditional algorithms, which require long pre-processing time. In recent years, neural networks as classifiers have come to notice. This thesis proposes the Hierarchical Classification technique, which contains several models with the same network structure. Our pre-processing method produces input data by using light curves and the period. We use OGLE-IV variable stars database to train and test the performance of Convolutional Neural Networks based on the Hierarchical Classification technique. We see that... 

In this thesis, we attempt to construct a model that can predict whether someone will retweet a tweet. For this purpose, we construct a machine learning model and we use Twitter’s network features as our model’s input. We collect about 1300 random tweets and their retweets to make retweet cascades. By collecting or calculating users’ features in each retweet cascade, we construct our desired input data for our model. We test both random forest and neural networks as our machine learning section of the model. Random forest is the most accurate of the two models, predicting retweet actions with an accuracy of 0.89. Additionally, we find out that two features of the network have the greatest...