Polybutylene Terephethalate-based Magnetic Nanofibers for Micro-solid phase Extraction of Triazines from Aquatic Media

, M.Sc. Thesis Sharif University of Technology Najafi Mobara, Mahdieh (Author) ; Bagheri, Habib (Supervisor)

Abstract

Porosity and high surface area is a profound concept for creating advance materials. Nanofibers are an important class of nanomaterials with high surface to volume ratio as a result of long lengths and nano-to-micro-scale diameters. Electrospinning is a simple method for producing nanofibers from different polymers and composites and prepare highly efficient sorbents. Polymeric nanocomposites are claimed to exhibit markedly improved mechanical, magnetic, thermal and electrical properties. Magnetic iron oxide nanoparticles have gained great attentions due to their low toxicity, stability and biocompatibility and have been extensively used to induce magnetic property to the desired sorbents....

Electrospun magnetic polybutylene terephthalate nanofibers for thin film microextraction

, Article Journal of Separation Science ; Volume 40, Issue 19 , 2017 , Pages 3857-3865 ; 16159306 (ISSN) Bagheri, H ; Najafi Mobara, M ; Roostaie, A ; Baktash, M. Y ; Sharif University of Technology

2017

Abstract

A thin film microextraction method using elecrospun magnetic polybutylene terephthalate nanofibers is developed and implemented to isolate some selected triazines. Due to the high mechanical stability of these nanofibers, they are repeatedly used under harsh magnetic stirring and ultrasonic conditions without any damage and structure degradation. The presence of magnetic nanoparticles within the nanofiber structure increases the extraction efficiency while the fibers could be collected by an external magnet. The synthesized nanocomposite showed strong affinity toward the selected analytes. Apart from the concentration of magnetic nanoparticles within the nanocomposite network, the effect of...

Test case prioritization using test case diversification and fault-proneness estimations

, Article Automated Software Engineering ; Volume 29, Issue 2 , 2022 ; 09288910 (ISSN) Mahdieh, M ; Mirian Hosseinabadi, S. H ; Mahdieh, M ; Sharif University of Technology

Springer 2022

Abstract

Regression testing activities greatly reduce the risk of faulty software release. However, the size of the test suites grows throughout the development process, resulting in time-consuming execution of the test suite and delayed feedback to the software development team. This has urged the need for approaches such as test case prioritization (TCP) and test-suite reduction to reach better results in case of limited resources. In this regard, proposing approaches that use auxiliary sources of data such as bug history can be interesting. We aim to propose an approach for TCP that takes into account test case coverage data, bug history, and test case diversification. To evaluate this approach we...

Multi-level decomposition approach for problem solving and design in software engineering

, Article Proceedings of the Annual Southeast Conference, 24 March 2011 through 26 March 2011 ; March , 2011 , Pages 249-254 ; 9781450306867 (ISBN) Najafi, A ; Niu, N ; Najafi, F ; Sharif University of Technology

2011

Abstract

In general, decomposition methods can facilitate the process of solving sophisticated and heterogonous problems in the area of software development and engineering. These approaches are assisting to decompose problems based on different disciplines, characteristics and functionalities that is results into increasing the computational efficiency (e.g. parallel processing/computing) and accelerate the software changing process, software modifications and error tracking. Essentially, these approaches contribute to the degree of modularity to decompose a complex problem into different sub-problems and to focus on local objectives. There are different approaches that are used to decompose a...

Deep Zero-shot Learning

, M.Sc. Thesis Sharif University of Technology Shojaee, Mohsen (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

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...

Multi-Modal Distance Metric Learning

, M.Sc. Thesis Sharif University of Technology Roostaiyan, Mahdi (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

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...

Fabrication and Characterization of Thermoplastic Starch Based Nanocomposite for Bone Scaffold

, M.Sc. Thesis Sharif University of Technology Mahdieh, Zahra (Author) ; Bagheri, Reza (Supervisor)

Abstract

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...

Unsupervised Domain Adaptation via Representation Learning

, M.Sc. Thesis Sharif University of Technology Gheisary, Marzieh (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

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...

Deep Learning for Multimodal Data

, M.Sc. Thesis Sharif University of Technology Rastegar, Sarah (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

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...

Deep Learning For Recommender Systems

, M.Sc. Thesis Sharif University of Technology Abbasi, Omid (Author) ; Soleimani, Mahdieh (Supervisor)

Abstract

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...

Adversarial Networks for Sequence Generation

, M.Sc. Thesis Sharif University of Technology Montahaei, Ehsan (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

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...

Improving Sampling Efficiency of Probabilistic Graphical Models

, M.Sc. Thesis Sharif University of Technology Mahdieh, Mohsen (Author) ; Beigy, Hamid (Supervisor)

Abstract

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...

Improving Graph Construction for Semi-supervised Learning in Computer Vision Applications

, M.Sc. Thesis Sharif University of Technology Mahdieh, Mostafa (Author) ; Rabiee, Hamid Reza (Supervisor)

Abstract

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...

A mathematical analysis of the mechanism of ultrasonic induced fluid percolation in porous media: Part I

, Article Proceedings - SPE Annual Technical Conference and Exhibition, 20 September 2010 through 22 September 2010 ; Volume 7 , September , 2010 , Pages 5833-5856 ; 9781617389641 (ISBN) Najafi, I ; Sharif University of Technology

2010

Abstract

This work concerns with experimentally and analytically investigation of free gravity drainage process in porous media under the influence of ultrasonic wave radiation. Glass beads ranges from 70 to 100 mesh sizes were packed and used in the tests. The working fluids consist of distilled water, kerosene and Doroud and Paidar crude oils as wetting and air as non-wetting phase. The measured oil recovery data along with Hagoort (1984) backward methodology were used to determine and to compare the relative permeability of wetting phases in presence and absence of ultrasonic radiation. In addition the relative permeability of non-wetting phases for both cases were calculated from inverse modeling...

Multi-label Classification by Considering Label Dependencies

, M.Sc. Thesis Sharif University of Technology Farahnak Ghazani, Fatemeh (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

In multi-label classification problems each instance can simultaneously have multiple labels. In these problems, in addition to the complexities of the input feature space we encounter the complexities of output label space. In the multi-label classification problems, there are dependencies between different labels that need to be considered. Since the dimensionality of the label space in real-world applications can be (very) high, most methods which explicitly model these dependencies are ineffective in practice and recently those methods that transform the label space into a latent space have received attention. A class of these methods which uses output space dimension reduction, first...

Adaptation for Evolving Domains

, M.Sc. Thesis Sharif University of Technology Bitarafan, Adeleh (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)

Abstract

Until now many domain adaptation methods have been proposed. A major limitation of almost all of these methods is their assumption that all test data belong to a single stationary target distribution and a large amount of unlabeled data is available for modeling this target distribution. In fact, in many real world applications, such as classifying scene image with gradually changing lighting and spam email identification, data arrives sequentially and the data distribution is continuously evolving. In this thesis, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced and propose the Evolving Domain Adaptation (EDA) method to classify...

Cancer Prediction Using cfDNA Methylation Patterns With Deep Learning Approach

, M.Sc. Thesis Sharif University of Technology Mahdavi, Fatemeh (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)

Abstract

Liquid biopsy includes information about the progress of the tumor, the effectiveness of the treatment and the possibility of tumor metastasis. This type of biopsy obtains this information by doing diagnosis and enumerating genetic variations in cells and cell-free DNA (cfDNA). Only a small fraction of cfDNA which might be free circulation tumor DNA (ctDNA) fragments, has mutations and is usually identified by epigenetic variations. On the other hand, the use of liquid biopsy has decreased, and tumors in the final stages are often untreatable due to the low accuracy in prediction of cancer. In this research, the aim is to predict cancer using cfDNA methylation patterns. We obtain these...

Answering Questions about Image Contents by Deep Networks

, M.Sc. Thesis Sharif University of Technology Chavoshian, Mohammad (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)

Abstract

Due to the recent advances in the learning of multimodal data, humans tend to use computer systems in order to solve more complex problems. One of them is Visual Question Answering (VQA), where the goal is finding the answer of a question asked about the visual contents of a given image. This is an interdisciplinary problem between the areas of Computer Vision, Natural Language Processing and Reasoning. Because of recent achievements of Deep Neural Networks in these areas, recent works used them to address the VQA task. In this thesis, three different methods have been proposed which adding each of them to existing solutions to the VQA problem can improve their results. First method tries to...

Conditional Text Generation with Neural Networks

, M.Sc. Thesis Sharif University of Technology Ali Hosseini, Danial (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

By the improvement of machine learning methods specially the Deep Learning in the last decade, there were expanding usage of these methods in Language Modeling task. As the essence of a language model is more basic, recently huge networks are trained with language model objective but fine-tuned on target tasks such as Question Answering, Sentiment Analysis and etc. which is a promising sign of its importance and usage in even other NLP tasks. However, this task still has severe problems. The Teacher Forcing based methods, suffer from the so-called exposure bias problem which is due to the train/test procedure discrepancy. Some solutions such as using Reinforcement Learning which has high...

Adversarial Robustness of Deep Neural Networks in Text Domain

, M.Sc. Thesis Sharif University of Technology Behjati, Melika (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)

Abstract

In recent years, neural networks have been widely used in most machine learning domains. However, it has been shown that these networks are vulnerable to adversarial examples. adversarial examples are small and imperceptible perturbations applied to the input which lead to producing wrong output and thus, fooling the network. This will become an important issue in security related applications of deep neural networks, such as self-driving cars and medical diagnostics. Since, in the wort-case scenario, even human lives could be threatened. Although, many works have focused on crafting adversarial examples for image data, only a few studies have been done on textual data due to the existing...