Sharif Digital Repository

In this thesis, after introducing some preliminary concepts, Stein’s method and Malliavin calculus is discussed. Our approach for introducing Malliavin calculus is based on isonormal Gaussian processes, which is more general and natural than Gaussian noises. After dealing with isonormal Gaussian processes, Wiener chaos and important operators of Malliavin calculus, namely differential, divergence and Ornstein-Uhlenbeck operators are discussed and some relation between them is studied. At last, some connections between Stein’s method and Malliavin calculus is developed. As a result, some exact asymptotics for central limit theorems on Gaussian functionals are obtained. These results are used,... 

Portfolio optimization is one of the most important issues in capital market and Mathematical Finance. Also in simiulations of financial instruments, in many cases the fluctuations are not independed so we can’t use standard Brownian motion for portfolio optimization and simiulations. In these cases, we should use another kind of Brownian motion which is called fractional Brownian motion. After introducing fractional Brownian motion in chapter 1, we will present its properties in chapter 2 , then at chapter 3 we’ll study stochastic calculus in fractional case and finally in chapter 4 after presenting Stochastic maximum Principle and applying it on a portfolio optimization problem, we will... 

In this thesis, the simulation of HEMT for high frequency applications has been explored. It has been tried to examine the changes such as: gate recess, T-shaped gate, changing the channel length and doping of buffer layer on the performance of the proposed device. Simulation results show that the best way to improve the device performance, in particular its cut-off frequency, is increase in buffer layer doping density. Because it significantly increase the saturation current, electron mobility inside the channel, the transconductance and the cut-off frequency. If we need to lower the noise, the T-shaped gate can also be used. Also, by change in doping of donor layer, amount of 1017 cm-3 is... 

The multilayer coating (Zn-Ni)1/(Zn-Ni)2 is deposited from a single bath with a zincate-sulfate compound and by changing the current density. The effect of current density and the number of layers on the chemical composition, surface morphology, roughness, phase structure, and corrosion resistance of multilayer coatings respectively using atomic absorption spectroscopy, FE-SEM microscope, roughness tests, X-ray analysis, and electrochemical techniques such as Tafel polarization and impedance was investigated and compared with single-layer coatings. Because the amount of nickel in the composition of the coatings is less than 27%, in all the studied currents, the deposition is anomalous. The... 

Nowadays population growth, people housing demand and urbanization tendency has increased the land and house prices. So the constructers tend to build apartments. Time consuming and heavy costs of building and renovation has made the owners to rebuild the damages of their houses in case of an accident. Time and cost are the main factors in construction which includes rebuilding. Buildings and especially apartments renovation is expensive and prolonged. So the rebuilding tendency has been noted more than ever. In this study it has been tried to control costs and time by efficient management to save the rebuilding time and money. Two kinds of questionnaire has been applied to study different... 

With the recent considerable developments in the Internet of Things (IoT), billions of resource-constrained devices are interconnected through the internet. Monitoring this huge number of IoT devices that are heterogeneous in terms of underlying communication protocols and data format is challenging. The majority of existing IoT device monitoring solutions heavily rely on centralized architectures. Since using centralized architectures comes at the expense of trusting an authority, it has several inherent drawbacks, including vulnerability to security attacks, lack of data privacy, and unauthorized data manipulation. Hence, a new decentralized approach is crucial to remedy these drawbacks.... 

In some of object recognition problems, labeled data may not be available for all categories. Zero-shot learning utilizes auxiliary information (also called signatures) describing each category in order to find a classifier that can recognize samples from categories with no labeled instance. On the other hand, with recent advances made by deep neural networks in computer vision, a rich representation can be obtained from images that discriminates different categorizes and therefore obtaining a unsupervised information from images is made possible. However, in the previous works, little attention has been paid to using such unsupervised information for the task of zero-shot learning. In this... 

In many real-world applications, data contain multiple input channels (e.g., web pages include text, images and etc). In these cases, supervisory information may also be available in the form of distance constraints such as similar and dissimilar pairs from user feedbacks. Distance metric learning in these environments can be used for different goals such as retrieval and recommendation. In this research, we used from dual-wing harmoniums to combining text and image modals to a unified latent space when similar-dissimilar pairs are available. Euclidean distance of data represented in this latent space used as a distance metric. In this thesis, we extend the dual-wing harmoniums for... 

This project aimed to fabricate the bone scaffolds with applying thermoplastic starch-based nano-biocomposites. The starting materials for this scaffold are as follows: thermoplastic starch, ethylene vinyl alcohol as the polymer matrix and nanoforsterite as the ceramic reinforcing phase. Furthermore, vitamin E was used as antioxidant for preserving starch against thermo-mechanical degradations. Likewise, 3D pore structure was developed using azo-dicarbonamide and water in injection moulding process. With blending thermoplastic starch and ethylene vinyl alcohol, some thermoplastic starch’s properties including degradation rate and water absorption were modified. In addition, having... 

The existing learning methods usually assume that training and test data follow the same distribution, while this is not always true. Thus, in many cases the performance of these learning methods on the test data will be severely degraded. We often have sufficient labeled training data from a source domain but wish to learn a classifier which performs well on a target domain with a different distribution and no labeled training data. In this thesis, we study the problem of unsupervised domain adaptation, where no labeled data in the target domain is available. We propose a framework which finds a new representation for both the source and the target domain in which the distance between these... 

Recent advances in data recording has lead to different modalities like text, image, audio and video. Images are annotated and audio accompanies video. Because of distinct modality statistical properties, shallow methods have been unsuccessful in finding a shared representation which maintains the most information about different modalities. Recently, deep networks have been used for extracting high-level representations for multimodal data. In previous methods, for each modality, one modality-specific network was learned. Thus, high-level representations for different modalities were extracted. Since these high-level representations have less difference than raw modalities, a shared... 

Collaborative fltering (CF) is one of the best and widely employed approaches in Recommender systems (RS). This approach tries to fnd some latent features for users and items so it would predict user rates with these features. Early CF methods used matrix factorization to learn users and items latent features. But these methods face cold start as well as sparsity problem. Recent years methods employ side information along with rating matrix to learn users and items latent features. On the other hand, deep learning models show great potential for learning effective representations especially when auxiliary information is sparse. Due to this feature of deep learning, we use deep learning to... 

Lots of essential structures can be modeled as sequences and sequences can be utilized to model the structures like molecules, graphs and music notes. On the other hand, generating meaningful and new sequences is an important and practical problem in different applications. Natural language translation and drug discovery are examples of sequence generation problem. However, there are substantial challenges in sequence generation problem. Discrete spaces of the sequence and challenge of the proper objective function can be pointed out.On the other, the baseline methods suffer from issues like exposure bias between training and test time, and the ill-defined objective function. So, the... 

Deep learning methods have become more popular in the past years. These methods use complex network architectures to model rich, hierarchical datasets. Although most of the research has been centered around Discriminative models, however, recently a lot of research is focused on Deep Generative Models. Two of the pioneering models in this field are Generative Adversarial Networks and Variational Auto-Encoders. In addition, knowing the structure of data helps models to search in a narrower hypothesis space. Most of the structure in datasets are models using Probabilistic Graphical Models. Using this structural information, one can achieve better parameter estimations. In the case of... 

Semi-supervised Learning (SSL) is an extremely useful approach in many applications where unlabeled data can be easily obtained. Graph based methods are among the most studied branches in SSL. Since neighborhood graph is a key component in these methods, we focus on methods of graph construction in this project. Graph construction methods based on Euclidean distance have the common problem of creating shortcut edges. Shortcut edges refer to the edges which connect two nearby points that are far apart on the manifold. Specifically, we show both in theory and practice that using geodesic distance for selecting and weighting edges results in more appropriate neighborhood graphs. We propose an...