Joining Spacetimes on Fractal Hypersurfaces

The theory of fractional calculus is attracting a lot of attention from mathematicians as well as physicists. The fractional generalisation of the well-known ordinary calculus is being used extensively in many fields, particularly in understanding stochastic process and fractal dynamics. In this paper, we apply the techniques of fractional calculus to study some specific modifications of the geometry of submanifolds. Our generalisation is applied to extend the Israel formalism which is used to glue together two spacetimes across a timelike, spacelike or a null hypersurface. In this context, we show that the fractional extrapolation leads to some striking new results. More precisely we demonstrate that, in contrast to the original Israel formalism, where many spacetimes can only be joined together through an intermediate thin hypersurface of matter satisfying some non- standard energy conditions, the fractional generalisation allows these spacetimes to be smoothly sewed together without any such requirements on the stress tensor of the matter fields. We discuss the ramifications of these results for spacetime structure and the possible implications for gravitational physics.Comment: 22 pages, 2 figure

Assessing the Role of Marketing at Earnings Announcement: Stock Market Response to Marketing Metrics Surprises

The explanatory power of earnings per share (eps) is on the decline as firms are focusing more on intangible assets and are disclosing more marketing metrics when they announce their earnings (e.g., subscribers for the telecom & media industry and monthly active users for social media industry). However, the performance of these marketing output metrics beyond market/analysts’ expectations (i.e., surprises) requires marketing resources, which may reduce current profitability but may also signal a higher future cash flow. Therefore, building on information economics, we assess if there is information content in marketing metric surprises, and how the stock market reacts to such surprises. Further, we argue that the information content of marketing metric surprises varies under different information signals by firms (strategic emphasis) and screening cues by investors (marketing expenditure). We also investigate the temporal variations in the effect of marketing metric surprises and also examine the relative importance of marketing metric surprises as compared to earnings surprises across multiple industries. We test the claims using an event study methodology around earnings announcement on S&P 1500 firms consisting of firms disclosing industry-specific marketing metrics and non-disclosing firms. We account for sample selection bias and correcting for potential endogeneity concerns of surprises marketing metrics. Our findings suggest that (1) although an increase in marketing metric surprise affects the stock market returns positively, (2) this effect is strengthened when firms signal strategic emphasis on value appropriation relative to value creation whereas (3) it is attenuated when investors screen for firms with higher unanticipated marketing expenditure, (4) the effect of marketing metric surprises increases over time whereas it decreases for earnings surprise, and (5) the effect of marketing metric surprise is higher in the telecom and media industry as compared to earnings surprises. The study helps to improve marketing accountability at the time of earnings announcement by improving the overall earnings quality of firms

Coarse-to-Fine Lifted MAP Inference in Computer Vision

There is a vast body of theoretical research on lifted inference in probabilistic graphical models (PGMs). However, few demonstrations exist where lifting is applied in conjunction with top of the line applied algorithms. We pursue the applicability of lifted inference for computer vision (CV), with the insight that a globally optimal (MAP) labeling will likely have the same label for two symmetric pixels. The success of our approach lies in efficiently handling a distinct unary potential on every node (pixel), typical of CV applications. This allows us to lift the large class of algorithms that model a CV problem via PGM inference. We propose a generic template for coarse-to-fine (C2F) inference in CV, which progressively refines an initial coarsely lifted PGM for varying quality-time trade-offs. We demonstrate the performance of C2F inference by developing lifted versions of two near state-of-the-art CV algorithms for stereo vision and interactive image segmentation. We find that, against flat algorithms, the lifted versions have a much superior anytime performance, without any loss in final solution quality.Comment: Published in IJCAI 201

Prevalence and determinants of co-use of alcohol and tobacco among men in working age group (18-59 years) in India

Background: The relationship between tobacco and alcohol use is very important in making strategies containing use of these products. Alcohol and tobacco use were studied separately in Indian studies, which can undermine their co-occurrences. The objective of the study is to know the prevalence and socio-economic determinants of co-occurrence of alcohol and tobacco use among men in working age groups. Data Sources: The data from the Study on Global Ageing and Adult Health (SAGE) Wave1 was used. It was a cross sectional household survey, which collected information for adults aged 18 years and above. Results: The prevalence of co-use of tobacco and alcohol among men aged 18-59 years was 23.5%. High use of tobacco was found among alcohol user. Age cohorts were also the important determinants of alcohol and tobacco use. The inverted U or V-shape relation was found between age groups and co-use of tobacco and alcohol. The prevalence was lowest for age groups 18-24 years and 50-59 years. Low education attainment and working in informal sector were the main risk factors for high co-use of these substances among men. The study also tried to look at the relationship between the co-use of alcohol and tobacco with some selected health indicators. Use of alcohol and tobacco was significantly related with under nutrition and increasing blood pressure levels among men in India. Conclusion: To contain the use of alcohol and tobacco to improve heath condition among adult men, policies and strategies must be form keeping the importance of their co-occurrences

Multi-objective optimization of an industrial styrene reactor using Harmony Search Algorithm

Multi-objective optimization of industrial styrene reactor is done using Harmony Search algorithm. Harmony search algorithm is a recently developed meta-heuristic algorithm which is inspired by musical improvisation process aimed towards obtaining the best harmony. Three objective functions – productivity, selectivity and yield are optimized to get best combination of decision variables for styrene reactor. All possible cases of single and multi-objective optimization have been considered. Pareto optimal sets are obtained as a result of the optimization study. Results reveal that optimized solution using harmony search algorithm gives better operating conditions than industrial practice