Sharif Digital Repository

(A) Chakraborty and co-workers have developed a green method for the bulk ring-opening polymerization of lactides in the presence of Cu(OAc)2 as a good catalyst to synthesize polymers with different end-terminal groups.3 These polymerizations are highly controlled leading to the formation of polymers with the expected number of average molecular weights and narrow molecular weight distribution. (B) Garden and co-workers have found that the oxidative addition of anilines with 1,4-naphthoquinone to give N-aryl-2-amino-1,4-naphthoquinones can be performed in the presence of catalytic amounts of copper(II) acetate.4 All the reactions are generally more efficient in that they are cleaner, higher... 

In mathematical programming, an important tool is the use of active set strategies to update the current solution of a linear system after a rank one change in the constraint matrix. We show how to update the general solution of a linear system obtained by use of the scaled ABS method when the matrix coefficient is subjected to a rank one change. © Australian Mathematical Society 2004  

Synthesis of dextran-graft-poly(hydroxyethyl methacrylate) gels as a new fluoride biosorbent was considered in this work. For this propose, the Taguchi experimental design method was used for optimizing the synthetic conditions of the gels to reach high level of fluoride absorbency. The effects of three main parameters including concentrations of monomer (hydroxyethyl methacrylate), crosslinking agent (ethylene glycol dimethacrylate), and initiator (ammonium persulfate) on the final properties of the prepared gels were investigated. The proposed mechanism for grafting and chemically crosslinking reactions was proved with equilibrium water absorption, Fourier-transformed infrared, scanning... 

A novel biopolymer-based superabsorbent hydrogel was synthesized through chemical crosslinking by graft copolymerization of partially neutralized acrylic acid onto the hydrolyzed collagen, in the presence of a crosslinking agent and a free radical initiator. The Taguchi method, a robust experimental design, was employed for the optimization of the synthesis reaction based on the swelling capacity of the hydrogels. This method was applied for the experiments and standard L16 orthogonal array with three factors and four levels were chosen. The critical parameters that have been selected for this study are crosslinker (N,N′-methylene bisacrylamide), initiator (potassium persulfate), and monomer... 

We adopt an innovation-driven framework and investigate the sparse/compressible distributions obtained by linearly measuring or expanding continuous-domain stochastic models. Starting from the first principles, we show that all such distributions are necessarily infinitely divisible. This property is satisfied by many distributions used in statistical learning, such as Gaussian, Laplace, and a wide range of fat-tailed distributions, such as student's-t and α-stable laws. However, it excludes some popular distributions used in compressed sensing, such as the Bernoulli-Gaussian distribution and distributions, that decay like exp (-O(|x|p)) for 1 < p < 2. We further explore the implications of... 

In this paper, we establish the connection between the Orthogonal Optical Codes (OOC) and binary compressed sensing matrices. We also introduce deterministic bipolar m × n RIP fulfilling ± 1 matrices of order k such that m ≤ script O sign (k(log2 n) log2 k/ln log2 k). The columns of these matrices are binary BCH code vectors where the zeros are replaced by -1. Since the RIP is established by means of coherence, the simple greedy algorithms such as Matching Pursuit are able to recover the sparse solution from the noiseless samples. Due to the cyclic property of the BCH codes, we show that the FFT algorithm can be employed in the reconstruction methods to considerably reduce the computational... 

Semantic technology is widely used in distributed computational environments to increase interoperability and machine readability of information through giving semantics to the underlying information and resources. Semantic-awareness, distribution and interoperability of new generation of distributed systems demand an authorisation model and framework that satisfies essential authorisation requirements of such environments. In this study, the authors propose an authorisation model and framework based on multi-security-domain architecture for distributed semantic-aware environments. The proposed framework is founded based on the MA(DL)2 logic, which enables policy specification and inference... 

Deep structures consisting of many layers of nonlinearities have a high potential of expressing complex relations if properly initialized. Autoencoders play a complementary role in training a deep structure by initializing each layer in a greedy unsupervised manner. Due to the high capacity presented by autoencoders, these structures need to be regularized. While mathematical regularizers (based on weight decay, sparsity, etc.) and structural ones (by way of, e.g., denoising and dropout) have been well studied in the literature, quite a few papers have addressed the problem of training autoencoder with non-smooth regularization. In this paper, we address the problem of training autoencoder... 

It has been shown that more accurate signal recovery can be achieved with low-coherence dictionaries in sparse signal processing. In this Letter, the authors extend the low-coherence attribute to receptive fields in convolutional neural networks. A new constrained formulation to train low-coherence convolutional neural network is presented and an efficient algorithm is proposed to train the network. The resulting formulation produces a direct link between the receptive fields of a layer through training procedure that can be used to extract more informative representations from the subsequent layers. Simulation results over three benchmark datasets confirm superiority of the proposed... 

Deep neural networks have presented superlative performance in many machine learning based perception and recognition tasks, where they have even outperformed human precision in some applications. However, it has been found that human perception system is much more robust to adversarial perturbation, as compared to these artificial networks. It has been shown that a deep architecture with a lower Lipschitz constant can generalize better and tolerate higher level of adversarial perturbation. Smooth regularization has been proposed to control the Lipschitz constant of a deep architecture and in this work, we show how a deep convolutional neural network (CNN), based on non-smooth regularization... 

The decentralized security management in a pervasive computing environment' requires apportioning the environment into several security domains. In each security domain' an administrator (we call it authority) is responsible for specifying the security policies of the domain. Overlapping of security domains results in the requirement of cooperative security management in the shared/ overlapping domains. To satisfy this requirement' we propose an abstract security model' as well as its supplementary calculus of composite authorities. The security model is based on deontic logic and is independent of the domains' heterogeneity. The model's policy language (we call it MASL) enables multiple... 

One of the proposed methods for recovery of a band-limited signal from its samples, whether uniform or nonuniform, is the so-called Frame Method. In this method the original signal is reconstructed by iterative use of sampling-filtering blocks. Convergence of this method for linear invertible operators has been previously proved. In this paper we show that this method for non-invertible periodic nonuniform samplings as well as non-invertible uniform samples of bandpass (or multi-band) signals will lead to the pseudo-inverse solution. Convergence conditions in case of additive noise will also be discussed. © 2008 Sampling Publishing  

Transformer networks, originally developed in the community of machine translation to eliminate sequential nature of recurrent neural networks, have shown impressive results in other natural language processing and machine vision tasks. Self-attention is the core module behind visual transformers which globally mixes the image information. This module drastically reduces the intrinsic inductive bias imposed by CNNs, such as locality, while encountering insufficient robustness against some adversarial attacks. In this paper we introduce K-sparse attention to preserve low inductive bias, while robustifying transformers against adversarial attacks. We show that standard transformers attend...