Matrix recovery using Split Bregman

In this paper we address the problem of recovering a matrix, with inherent low rank structure, from its lower dimensional projections. This problem is frequently encountered in wide range of areas including pattern recognition, wireless sensor networks, control systems, recommender systems, image/video reconstruction etc. Both in theory and practice, the most optimal way to solve the low rank matrix recovery problem is via nuclear norm minimization. In this paper, we propose a Split Bregman algorithm for nuclear norm minimization. The use of Bregman technique improves the convergence speed of our algorithm and gives a higher success rate. Also, the accuracy of reconstruction is much better even for cases where small number of linear measurements are available. Our claim is supported by empirical results obtained using our algorithm and its comparison to other existing methods for matrix recovery. The algorithms are compared on the basis of NMSE, execution time and success rate for varying ranks and sampling ratios

Quest for Identity in Bharati Mukherjee’s Novel Leave It to Me

Quest for identity or identity crisis has achieved propel in the Post Colonial literature. Post colonial literature can be identified by its discussion of cultural identity. It attempts to restore the original culture, conformity to the culture presented by the settlers or the creation of a new culture which combines both the left and the adopted. Indian English fiction deals eventually with the predicament, rising due to multi-culturalism and intercultural interactions. When a man is uprooted, he mislays the sense of belongingness and thus suffers from a sense of insecurity or identity crises. &nbsp

EFFECT OF AAHAR AND VIHAR IN NCDS W.S.R TO MODERN ERA

NCD is medical conditions that is not caused by infectious agent but are caused as a direct result of diet, life style and environmental factors. NCDs can refers to chronic diseases which last for long periods of time and progress slowly such as autoimmune disease, cardiovascular diseases, metabolic diseases and chronic kidney diseases. NCDs are leading cause of death in the world representing 63% of all annual deaths. The major cause of NCD is eating unhealthy foods like food so much sweet, high salt, high fat, other junk food and fruits persevered with chemical fertilizer and drinking excess amount of alcohol. Doing no physical activity have increased working hours, high stress level, are major cause of NCDs.Ayurveda is science of life it is mentioned that the 3 pillars of healthy and long life are proper Aahar, Nidra, Bhrahmcharya of our body. Our diet is an essential factor for the formation of body. Charak says that consuming improper diet in improper way is the main cause of disease this is explained under Ashtaaharvidhivisheshayatan, Viruddhaahar. Use of incompatible food leads to skin disorder, GIT disorder, Diabetes, obesity, hence these should be avoided. Ayurveda imphasizes regulation of Aahar Vihar in the form of Ashtaaharvidhivisheshayatan, Dincharya, Ritucharya. Ayurveda can definitely yield reliable efficacious result when applied to all manner of chronic diseases

Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study

Mixup is a popular data augmentation technique based on creating new samples by linear interpolation between two given data samples, to improve both the generalization and robustness of the trained model. Knowledge distillation (KD), on the other hand, is widely used for model compression and transfer learning, which involves using a larger network's implicit knowledge to guide the learning of a smaller network. At first glance, these two techniques seem very different, however, we found that "smoothness" is the connecting link between the two and is also a crucial attribute in understanding KD's interplay with mixup. Although many mixup variants and distillation methods have been proposed, much remains to be understood regarding the role of a mixup in knowledge distillation. In this paper, we present a detailed empirical study on various important dimensions of compatibility between mixup and knowledge distillation. We also scrutinize the behavior of the networks trained with a mixup in the light of knowledge distillation through extensive analysis, visualizations, and comprehensive experiments on image classification. Finally, based on our findings, we suggest improved strategies to guide the student network to enhance its effectiveness. Additionally, the findings of this study provide insightful suggestions to researchers and practitioners that commonly use techniques from KD. Our code is available at https://github.com/hchoi71/MIX-KD.Comment: To be presented at WACV 202

Association between obesity and selected morbidities: A study of BRICS

Context: Over the past few decades, obesity has reached epidemic proportions, and is a major contributor to the global burden of chronic diseases and disability. There is little evidence on obesity related co-morbidities in BRICS countries. Objectives: The first objective is to examine the factors associated with overweight and obesity in four of the five BRICS countries (China, India, Russia and South Africa). The second is to examine the linkage of obesity with selected morbidities. Methods: We used data from the Study on Global Ageing and Adult Health (SAGE) survey conducted in China, India, Russia and South Africa during 2007-10. Respondents with a body mass index (BMI)>= 25 but = 30 as obese. Bivariate analysis, binary logistic regression and multinomial logistic regression are used in the analysis. The morbidities included in the analysis are Hypertension, Diabetes, Angina, Stroke, Arthritis and Depression. Results: The prevalence of obesity was highest in South Africa (35%) followed by Russia (27%), China (5%) and India (3%). The prevalence of obesity was significantly higher in females as compared to males in all the countries. While the wealth quintile was significantly associated with obesity in India, Russia and South Africa, engaging in work requiring physical activity was significantly associated with obesity in China and South Africa. Overweight/obesity was significantly associated with morbidities such as Hypertension, Angina, Diabetes and Arthritis in all the four countries. In comparison, overweight/obesity was not associated with Stroke and Depression in any of the four countries included in the analysis. Conclusion: The data demonstrates a high prevalence of obesity in South Africa and Russia. Overweight/obesity was significantly associated with Hypertension, Angina, Diabetes and Arthritis