Sharif Digital Repository

With the increasing volume of data, the best approach for learning from this data is to exploit an online learning algorithm. Online ensemble methods take advantage of an ensemble of classifiers to predict labels of data. Prediction with expert advice is a well-studied problem in the online ensemble learning literature. The weighted majority and the randomized weighted majority (RWM) algorithms are two well-known solutions to this problem, aiming to converge to the best expert. Since among some expert, the best one does not necessarily have the minimum error in all regions of data space, defining specific regions and converging to the best expert in each of these regions will lead to a... 

Bag of words models for feature extraction have demonstrated top-notch performance in image classification. These representations are usually accompanied by a coding method. Recently, methods that code a descriptor giving regard to its nearby bases have proved efficacious. These methods take into account the nonlinear structure of descriptors, since local similarities are a good approximation of global similarities. However, they confine their usage of the global similarities to nearby bases. In this paper, we propose a coding scheme that brings into focus the manifold structure of descriptors, and devise a method to compute the global similarities of descriptors to the bases. Given a local... 

Tracking by detection is the topic of recent research that has received considerable attention in computer vision community. Mainly off-line classification methods have been used, however, they perform weakly in the case of appearance changes. Training theclassifier incrementally and in an online manner solves this problem, but nevertheless, raises drifting due to soft or hard labeling in the online adaptation. In this paper a novel semi-supervised online tracking algorithm based on manifold assumption is proposed. Target object and background patches lie near low-dimensional manifolds. This motivates us to make use of the intrinsic structure of data in classification, and benefit from the... 

Feature coding has received great attention in recent years as a building block of many image processing algorithms. In particular, the importance of the locality assumption in coding approaches has been studied in many previous works. We review this assumption and claim that using the similarity of data points to a more global set of anchor points does not necessarily weaken the coding method, as long as the underlying structure of the anchor points is considered. We propose to capture the underlying structure by assuming a random walker over the anchor points. We also show that our method is a fast approximation to the diffusion map kernel. Experiments on various data sets show that with a... 

This paper introduces a novel coding scheme based on the diffusion map framework. The idea is to run a t-step random walk on the data graph to capture the similarity of a data point to the codebook atoms. By doing this we exploit local similarities extracted from the data structure to obtain a global similarity which takes into account the nonlinear structure of the data. Unlike the locality-based and sparse coding methods, the proposed coding varies smoothly with respect to the underlying manifold. We extend the above transductive approach to an inductive variant which is of great interest for large scale datasets. We also present a method for codebook generation by coarse graining the data... 

High molecular weight polyacrylamide (PAM) nanoparticle dispersions are products with wide application possibilities, the most important of which is in petroleum industry such as drilling fluid and flooding agent in enhanced oil recovery. For that aim, it is necessary to achieve complete control of the final dispersion and polymer properties during the synthesis step. In this work, PAMs were synthesized by inverse emulsion polymerization of aqueous acrylamide solution in cyclohexane in the presence of emulsifier mixture of Span 20 and Span 80. We present a comprehensive study of the effects of variation of all important reaction conditions (agitation rate, reaction time and temperature,... 

Amphiphilic copolymers consist of hydrophilic and hydrophobic monomer units have attracted great technological attention recently, owing to their unique properties and their ability to stabilize various interfaces in aqueous systems. This paper presents a novel and facile approach to produce spherical polyacrylamide, polystyrene and hydrophobically modified polyacrylamide (HM-PAM), as one of the most important type of amphiphilic copolymers, using a continuous aerosol-photopolymerization for the first time. To this end, the monomer droplets were generated by an atomizer, then photopolymerization was initiated ‘‘in flight’’ by ultraviolet (UV) irradiation of the aerosol monomer droplets... 

With the decline in oil discoveries over recent decades, it is believed that enhanced oil recovery (EOR) technologies will play a key role to meet energy demand in the coming years. Polymer flooding is used commonly worldwide as an EOR process. In this work, we propose the synthesis of protected polyacrylamide (PAM) nanoparticles (PPNs) with a hydrophobic polystyrene (PSt) shell by one-pot two-step inverse emulsion polymerization, in which the PSt shell was created by surface polymerization. The shell protects the active PAM chains from premature degradation caused by the harsh environment in the reservoirs, controls the release of the chains as rheological modifiers, and additionally, it... 

Abstract original image Smart Enhanced Oil Recovery Process using Core–Shell Nanoparticles: The cover image denotes schematically the enhanced oil recovery process by flooding using an aqueous dispersion of core–shell nanoparticles made up of protected polyacrylamide nanostructures (PPNs). Y. Tamsilian and his colleagues at Sharif University of Technology (Iran) and the Institute for Polymer Material (POLYMAT, Spain) demonstrated the synthesis of polyacrylamide nanoparticles protected with a hydrophobic polystyrene shell by using a one-pot, two-step inverse emulsion polymerization technique, where the polystyrene shell was created by surface polymerization. The presence of the hydrophobic... 

Semi-Supervised Learning (SSL) has become a topic of recent research that effectively addresses the problem of limited labeled data. Many SSL methods have been developed based on the manifold assumption, among them, the Local and Global Consistency (LGC) is a popular method. The problem with most of these algorithms, and in particular with LGC, is the fact that their naive implementations do not scale well to the size of data. Time and memory limitations are the major problems faced in large-scale problems. In this paper, we provide theoretical bounds on gradient descent, and to overcome the aforementioned problems, a new approximate Newton's method is proposed. Moreover, convergence...