Direct marketing: challenges and prospects from an experience On Dot com systems

This internship report is submitted in a partial fulfillment of the requirements for the degree of Bachelor of Business Administration, 2015.Cataloged from PDF version of Internship report.Dot com system Ltd is one of the growing IT firm in Bangladesh, started its operations from 2007. Dot com system Ltd offers all kinds of Commercial Corporate and Personal web based services include software solutions. Dot com system is one of the IT firm which help all kind of services that an IT firm offers. For every business Clients are very important. IT firm in Bangladesh has grown meaningfully as “service-industry”. My report is based on the Brand marketing of Dot com system Ltd, in this report I tried to lift up a general condition of Brad marketing of as new growing IT firm Promised and delivered to the customers and the process how to build up their brand as prominent brand for customer. The first part of the report contains information of the organization itself, the products and services that it firm is providing to the customer and vision for the future. The second part of the report contains the nature of job; my duties and responsibilities towards the job and some suggestions how they can improve their current branding as a growing business organization. The third part covers the development part that I was involved in my internship. In this part I have discussed about Brand marketing of Dot com system Limited, employee’s performance and cooperation to the customer. The last part covers the closing part. Before representation any conclusion based on this report it may be noted that there might be lack in data, but still it may be useful for designing any other study.Saima SharminB. Business Administratio

Impact of left atrial appendage closure on cardiac functional and structural remodeling: A difference-in-difference analysis of propensity score matched samples

Background: Although the safety and efficacy of left atrial (LA) appendage (LAA) closure (LAAC) in nonvalvular atrial fibrillation (NVAF) patients have been well documented in randomized controlled trials and real-world experience, there are limited data in the literature about the impact of LAAC on cardiac remodeling. The aim of the study was to examine the impact of LAAC on cardiac functional and structural remodeling in NVAF patients. Methods: Between March 2014 and November 2016, 47 NVAF patients who underwent LAAC were included in this study (LAAC group). A control group (non-LAAC group) was formed from 141 NVAF patients without LAAC using propensity score matching. The difference-in-difference analysis was used to evaluate the difference in cardiac remodeling between the two groups at baseline and follow-up evaluations. Results: The LAAC group had a larger increase in LA dimension, volume and volume index than the non-LAAC group (+3.9 mm, p = 0.001; +9.7 mL, p = 0.006 and +5.9 mL/m2, p = 0.011, respectively). Besides, a significant increase in E and E/e’ ratio was also observed in the LAAC group (+14.6 cm/s, p = 0.002 and +2.3, p = 0.028, respectively). Compared with the non-LAAC group, left ventricular (LV) ejection fraction and fractional shortening decreased in LAAC patients, but were statistically insignificant (–3.5%, p = 0.109 and –2.0%, p = 0.167, respectively). Conclusions: There were significant increases in LA size and LV filling pressure among NVAF patients after LAAC. These impacts of LAAC on cardiac functional and structural remodeling may have some clinical implications that need to be addressed in future studies

Towards Efficient Optimization Methods: Combinatorial Optimization and Deep Learning-Based Robust Image Classification

Every optimization problem shares the common objective of finding a minima/maxima, but its application spans over a wide variety of fields ranging from solving NP-hard problems to training a neural network. This thesis addresses two crucial aspects of the above-mentioned fields. The first project is concerned with designing a hardware-system for efficiently solving Traveling Salesman Problem (TSP). It involves encoding the solution to the ground state of an Ising Hamiltonian and finding the minima of the energy landscape. To that end, we i) designed a stochastic nanomagnet-based device as a building block for the system, ii) developed a unique approach to encode any TSP into an array of these blocks, and finally, iii) established the operating principle to make the system converge to an optimal solution. We used this method to solve TSPs having more than 600 nodes. The next parts of the thesis deal with another genre of optimization problems involving deep neural networks (DNN) in image-classification tasks. DNNs are trained by finding the minima of a loss landscape aimed at mapping input images to a set of discrete labels. Adversarial attacks tend to disrupt this mapping by corrupting the inputs with subtle perturbations, imperceptible to human eyes. Although it is imperative to deploy some external defense mechanisms to guard against these attacks, the defense procedure can be aided by some intrinsic robust properties of the network. In the quest for an inherently resilient neural network, we explored the robustness of biologically-inspired Spiking Neural Networks (SNN) in the second part of the thesis. We demonstrated that accuracy degradation is less severe in SNNs than in their non-spiking counterparts. We attribute this robustness to two fundamental characteristics of SNNs: (i) input discretization and (ii) leak rate in Leaky-Integrate-Fire neurons and analyze their effects. s mentioned beforehand, this intrinsic robustness is merely an aiding tool to external defense mechanisms. Adversarial training has been established as the stat-of-the-art defense to provide significant robustness against existing attack techniques. This method redefines the boundary of the neural network by augmenting the training dataset with adversarial samples. In the process of achieving robustness, we are faced with a trade-off: a decrease in the prediction accuracy of clean or unperturbed data. The goal of the last section of my thesis is to understand this setback by using Gradient Projection-based sequential learning as an analysis tool. We systematically analyze the interplay between clean training and adversarial training on parameter subspace. In this technique, adversarial training follows clean training task where the parameter update is performed in the orthogonal direction of the previous task (clean training). It is possible to track down the principal component directions responsible for adversarial training by restricting clean and adversarial parameter update to two orthogonal subspaces. By varying the partition of subspace, we showed that the low-variance principal components are not capable of learning adversarial data, rather it is necessary to perform parameter update in a common subspace consisting of higher variance principal components to obtain significant adversarial accuracy. However, disturbing these higher variance components causes the decrease in standard clean accuracy, hence the accuracy-robustness trade-off. Further, we showed that this trade-off is worsened when the network capacity is smaller due to under-parameterization effect

Percutaneous closure of perimembranous ventricular septal defect using patent ductus arteriosus occluders.

OBJECTIVES:To assess the safety and efficacy of percutaneous closure of perimembranous ventricular septal defect (PmVSD) using patent ductus arteriosus (PDA) occluders. BACKGROUND:Widespread use of conventional PmVSD closure devices has been limited by unacceptable high rate of complete heart block (CHB). The elegant design of PDA occluders is supposed to ease implantation, increase closure rate and minimize damage to adjacent structures. Thus, PDA occluders may reduce complications, especially the CHB, and offer a good alternative for PmVSD closure. METHOD:From September 2008 to October 2015, patients who underwent attempted percutaneous VSD closure using PDA occluders were included in the study. Patient demographics, echocardiography measurements, procedure details and follow-up data until October 2017 were collected. RESULTS:In total, 321 patients with a mean age of 15.5±12.6 years and mean a weight of 33.3±20.5 kg were included in this study. The mean defect size was 4.8±2.1 mm. Implantation was successful in 307 (95.6%) patients. The median follow-up time was 63 months (24 to 108 months). The closure rates were 89.5%, 91.5%, and 99.3% after the procedure 24 hours, 6 months and 2 years, respectively. Major complications occurred in 5 (1.7%) patients during the procedure and follow-up, including persistent CHB in 2 (0.7%) patients and device embolization in 3 (1.0%) patients. No death, disability, or other major complication was detected. CONCLUSION:Percutaneous closure of PmVSD using PDA occluders is feasible, safe and efficacious in selected patients