SamBaTen: Sampling-based Batch Incremental Tensor Decomposition

Tensor decompositions are invaluable tools in analyzing multimodal datasets. In many real-world scenarios, such datasets are far from being static, to the contrary they tend to grow over time. For instance, in an online social network setting, as we observe new interactions over time, our dataset gets updated in its "time" mode. How can we maintain a valid and accurate tensor decomposition of such a dynamically evolving multimodal dataset, without having to re-compute the entire decomposition after every single update? In this paper we introduce SaMbaTen, a Sampling-based Batch Incremental Tensor Decomposition algorithm, which incrementally maintains the decomposition given new updates to the tensor dataset. SaMbaTen is able to scale to datasets that the state-of-the-art in incremental tensor decomposition is unable to operate on, due to its ability to effectively summarize the existing tensor and the incoming updates, and perform all computations in the reduced summary space. We extensively evaluate SaMbaTen using synthetic and real datasets. Indicatively, SaMbaTen achieves comparable accuracy to state-of-the-art incremental and non-incremental techniques, while being 25-30 times faster. Furthermore, SaMbaTen scales to very large sparse and dense dynamically evolving tensors of dimensions up to 100K x 100K x 100K where state-of-the-art incremental approaches were not able to operate

Neural Predictors of Exercise Adherence

Exercise is an important factor in maintaining physical and cognitive health throughout the lifespan. However, adherence to exercise regimens is poor with approximately 50% of older adults dropping out within 6 months, which makes it difficult to observe exercise-induced biological changes. Unfortunately, there are few known predictors for exercise adherence, but it is likely that a combination of social-cognitive factors, including self-efficacy, social support, personality traits, executive functions, and self-regulation all relate to exercise adherence. Importantly, all of these factors may rely upon the structural integrity of brain networks. In this study we tested whether grey matter volume prior to the initiation of an exercise intervention would predict adherence to the intervention. Participants included 159 adults aged 60-80 that were randomly assigned to either a moderate-intensity aerobic walking condition or a non-aerobic stretching and toning condition. Participants engaged in supervised exercise 3 times per week for 12 months. Structural magnetic resonance images were collected on individuals before randomization and used for analysis. An optimized voxel based morphometry (VBM) protocol was used to analyze gray matter volume using FSL. We used ordinary least squares regression models with bootstrapping using the Bootstrap Regression Analysis of Voxelwise Observations (BRAVO) toolbox to test the association between voxel-based grey matter volume and exercise adherence. We found a broad array of regions that significantly predicted exercise adherence (p<.01), including medial prefrontal cortex, superior parietal cortex, inferior temporal cortex, and cerebellum. Greater volume in these regions explained 20% of variance in adherence, above and beyond variance explained by self-efficacy. Our results suggest that greater gray matter volume predicts more successful adherence to a 12-month supervised exercise regimen

Associations Among Ethnicity, Gender, Age, Age of First Drink, and Drinking Behavior Among High School Students

Moderation management theory was employed to assess whether gender, ethnicity, age, and age of first drink were associated with drinking among adolescents. The statistically significant model distinguished between adolescents who reported moderate versus binge drinking. Age of first drink made a statistically significant main effect contribution to the model.https://scholarworks.waldenu.edu/archivedposters/1110/thumbnail.jp

Business model innovation for sustainable development: green technologies and BOP (Bottom of Pyramid) in emerging countries: South Africa and India

Doctoral research dissertation in the fulfilment of the requirements for the degree Doctor of Philosophy in Marketing at the Faculty of Commerce, Law and Management, University of the Witwatersrand, Johannesburg, South Africa, 2016Globally, a vision exists of an economy which produces social, environmental and economic benefits, viz-a-viz three pillars of sustainable development, for all the individuals, communities and society at large. It also focuses on the development of the sustainable use of natural resources, to achieve a greater enviable society, therefore giving rise to the green economy (Bigg 2011). To make businesses sustainable, companies are increasingly focusing on green innovation, sustainable business solutions and re-inventing their business models, and expanding to untapped markets such as the bottom of the pyramid (BOP), consisting of more than four billion potential consumers (Farinelli, Bottini, Akkoyunlu & Aerni, 2011). Most research shows growth opportunities of green products in the bottom of pyramid (Olsen & Boxenbaum, 2009), and has increasingly created deliberations all over the world. Also, companies from both developed and developing countries are becoming increasingly interested in BOP. To successfully target the BOP with ‘green’ technologies, companies focus their business models on innovation, sustainability and economic profit, instead of gross margins (Prahalad & Hart, 2008). Very limited research evidence is present that links all these concepts together. And therefore, created an interest to examine how integration of green technology bring changes in business model innovation (BMI) for sustainability at BOP markets. The linkage between concepts - BMI, BOP and green technology, to bring sustainable development, has not been sufficiently explored, and especially with focus on emerging economies like South Africa and India. Therefore, the present research has three fold purposes. Firstly, to analyse and understand factors affecting the existing business models of various companies with green technologies targeting BOP markets for sustainable development. Secondly, the research brings an identification and understanding of number of key factors related to BMI, BOP markets and green technologies for sustainable development, and proposes a conceptual framework based on a series of underpinning relationships among these factors. Thirdly, it testifies the conceptualized theoretical framework on green business model innovation for sustainable development for BOP markets, among large companies. The primary objective of research study is to design a right green business model innovation across companies with green technologies for BOP markets. The secondary objective is to identify and compare the differences and similarities of green business model innovation for BOP markets of both South Africa and India. The present research undertakes a sequential exploratory mixed method approach, and is carried out in three phases: Phase 1: Exploration and study of business model innovation of identified industries/sectors with green technologies, targeting BOP segment for sustainable development, using qualitative research methods to formulate multiple cases. Phase 2: Identification of underpinning factors related to BMI, sustainable development and BOP consumers for green technologies; using qualitative methods and content analysis of results from phase 1, leading to design and development of theoretical framework of green business model innovation for South Africa and India. Phase 3: Testing of conceptualized framework of green business model innovation for sustainable development, using quantitative research methods. The present research tests underpinning factors of emerging green business model innovation for sustainable development, resulting from the qualitative phase, and is used to expand and generalize qualitative findings by using quantitative methods. The findings resulted in linking three theoretical emerging topics in the literature: business model innovation (BMI), green technology for sustainability and BOP. Four cases are developed through 33 face-to-face in-depth interviews with company top executives, using multiple case study approach. Each case comprised of sustainable business model innovation, representing comparison between South Africa and India, across four industries, namely, Energy, Banking, FMCG/Durable sectors and Cloud Computing. Qualitative content analysis and findings resulted in formation of themes and sub-themes and proposed prepositions, depicting the relationship between BMI, BOP, and green technology for sustainability. These prepositions aided in development of conceptual framework and proposed nine hypotheses. The conceptual model is quantitatively surveyed on 206 employees of large companies with focus on BOP markets. The quantitative findings supported all nine hypotheses. Therefore, indicating that integration of green technology is associated with performance of green product/service innovation and green process innovation in a company. Likewise, business model innovation variables; customer interface, infrastructure management and financial aspects, positively impacts sustainability of business model. The contribution of this thesis is in the development of green business model innovation for sustainable development, with focus on BOP markets. This adds to the contextual knowledge and empirical literature on business model innovation, green technologies and BOP markets. Theoretically, it brings better understanding of these concepts, and provides a basis of further research highlighting the importance of innovation while taking account of green economy and BOP. The findings provide marketing practitioners with better understanding of strategies that can be employed to innovate and change their own business models to incorporate green and sustainable initiative for BOP markets.XL201

Arbitrage in Political Prediction Markets

Online prediction markets are a powerful tool for aggregating information and show promise as predictive tools for uncertain outcomes, from sporting events to election results. However, these markets only serve as effective prediction tools so long as the market pricing remains efficient. We analyze the potential arbitrage profits derived from such mispricings in two leading American political prediction markets, PredictIt (for the 2016 and 2020 elections) and the Iowa Electronic Markets (for the 2016 election), to quantify the degree of mispricing and to show how market design can contribute to price distortion. We show that contracts hosted by PredictIt, compared to the IEM, are chronically mispriced, with large arbitrage profits in the 2016 election markets and non-negligible profits for the 2020 markets. We discuss the role of profit fees and contract limits, the primary differences between the PredictIt and IEM, in distorting pricing on PredictIt by limiting the ability of traders to capture arbitrage profits. Additionally, we examine the association between arbitrage and margin-linking, increased liquidity, and the number of unique contracts PredictIt's markets. This research provides cautionary evidence of potential inefficiencies in prediction markets with the intention of improving market implementation and enhancing market predictiveness

Cryptography using Artificial Neural Networks

A Neural Network is a machine that is designed to model the way in which the brain performs a task or function of interest. It has the ability to perform complex computations with ease. The objective of this project was to investigate the use of ANNs in various kinds of digital circuits as well as in the field of Cryptography. During our project, we have studied different neural network architectures and training algorithms. A comparative study is done between different neural network architectures for an Adder and their merits/demerits are discussed. Using a Jordan (Recurrent network), trained by back-propagation algorithm, a finite state sequential machine was successfully implemented. The sequential machine thus obtained was used for encryption with the starting key being the key for decryption process. Cryptography was also achieved by a chaotic neural network having its weights given by a chaotic sequence

The Internal Administration of Lord Lytton, With Special Reference to Social and Economic Policy, 1876-1880.

This is a study of some aspects of Lord Lytton's social and economic policy. Lytton was very anxious to reduce, if not totally abolish, the cotton duties. His anxiety was due primarily to his wish to help the Conservative Party through this tariff reform. Besides, he believed in the economic wisdom of free trade principles. It was "because of his belief in the latter principles that he did not allow any interference in the free play of private trade during the famine in Bombay and Madras. In addition, he wanted the two famines to be managed as economically as possible, for he knew that unless there were a surplus, he could not reduce the cotton duties. This, in its turn, led to much trouble between the Supreme Government and the two Presidency Governments. The chief aim of Lytton's social policy was his belief in the wisdom of befriending the Indian aristocracy. With this purpose in view, he constituted the statutory Civil Service, which was to attract and employ the sons of rich and influential Indians. At the same time, his action in the Puller Case demonstrated that he was not prepared to tolerate the ill-treatment of Indians, especially the lower classes, by the Europeans resident in India. But he was disposed to regard the educated Indians with suspicion, for they were the authors of the semi-seditious articles in the vernacular Press. And to curt this, he passed the Vernacular Press Act. This study is based on Lord Lytton's private papers, as well as on official archives, contemporary newspapers and published books

A minimality property for knots without Khovanov 2-torsion

A conjecture of Shumakovitch states that every nontrivial knot has 2-torsion in its Khovanov homology. We show that if a knot $K$ has no 2-torsion in its Khovanov homology, then the rank of its reduced Khovanov homology is minimal among all knots obtainable from $K$ by a proper rational tangle replacement. It follows, for example, that unknotting number 1 knots have 2-torsion in their Khovanov homology.Comment: 3 page