Review of High-Quality Random Number Generators

This is a review of pseudorandom number generators (RNG's) of the highest quality, suitable for use in the most demanding Monte Carlo calculations. All the RNG's we recommend here are based on the Kolmogorov-Anosov theory of mixing in classical mechanical systems, which guarantees under certain conditions and in certain asymptotic limits, that points on the trajectories of these systems can be used to produce random number sequences of exceptional quality. We outline this theory of mixing and establish criteria for deciding which RNG's are sufficiently good approximations to the ideal mathematical systems that guarantee highest quality. The well-known RANLUX (at highest luxury level) and its recent variant RANLUX++ are seen to meet our criteria, and some of the proposed versions of MIXMAX can be modified easily to meet the same criteria.Comment: 21 pages, 4 figure

Customer perception of switch-feel in luxury sports utility vehicles

Successful new product introduction requires that product characteristics relate to the customer on functional, emotional, aesthetic and cultural levels. As a part of research into automotive human machine interfaces (HMI), this paper describes holistic customer research carried out to investigate how the haptics of switches in luxury sports utility vehicles (SUVs) are perceived by customers. The application of these techniques, including an initial proposal for objective specifications, is addressed within the broader new product introduction context, and benefits described. One-hundred and one customers of SUVs assessed the feel of automotive push switches, completing the tasks both in, and out of vehicles to investigate the effect of context. Using the semantic differential technique, hedonic testing, and content analysis of customers’ verbatim comments, a holistic picture has been built up of what influences the haptic experience. It was found that customers were able to partially discriminate differences in switch-feel, alongside considerations of visual appearance, image, and usability. Three factors named ‘Affective’, ‘Robustness and Precision’, and ‘Silkiness’ explained 61% of the variance in a principle components analysis. Correlations of the factors with acceptance scores were 0.505, 0.371, and 0.168, respectively

Understanding customers' holistic perception of switches in automotive human–machine interfaces

For successful new product development, it is necessary to understand the customers' holistic experience of the product beyond traditional task completion, and acceptance measures. This paper describes research in which ninety-eight UK owners of luxury saloons assessed the feel of push-switches in five luxury saloon cars both in context (in-car) and out of context (on a bench). A combination of hedonic data (i.e. a measure of ‘liking’), qualitative data and semantic differential data was collected. It was found that customers are clearly able to differentiate between switches based on the degree of liking for the samples' perceived haptic qualities, and that the assessment environment had a statistically significant effect, but that it was not universal. A factor analysis has shown that perceived characteristics of switch haptics can be explained by three independent factors defined as ‘Image’, ‘Build Quality’, and ‘Clickiness’. Preliminary steps have also been taken towards identifying whether existing theoretical frameworks for user experience may be applicable to automotive human–machine interfaces

Fashion Conversation Data on Instagram

The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of {24,752} labeled images on fashion conversations, containing visual and textual cues, available for the research community.Comment: 10 pages, 6 figures, This paper will be presented at ICWSM'1

Effects of White Space on Consumer Perceptions of Value in E-Commerce

As e-commerce becomes an increasingly large industry, questions remain about how the isolated effects of design elements on websites influence consumer perceptions and purchasing behavior. This study used a quantitative approach to measuring the effect of a ubiquitous element of design, white space, on the perception of the monetary value of individual items. White space is a key component of design and website usability, yet it has been shown to be related to the perception of luxury. Little is known about the direct relationship between manipulation of white space and the outcomes on consumer perceptions of value in an e-commerce context. This study found no significant difference between two levels of total white space area (large vs. small) measured by participants\u27 perceived cost of items (chairs). In contrast, while holding total white space constant, the effect of white space distance between images was significant for males but not for females. Additionally, no significant relationship between gender and frequency of online shopping behavior was found, χ2(1) = 3.19, p = .07, ϕ = .17. Gender and amount of time spent per month online were significantly related, χ2(1) = 6.21, p = .013, ϕ = .24

When Do Luxury Cars Hit the Road? Findings by A Big Data Approach

In this paper, we focus on studying the appearing time of different kinds of cars on the road. This information will enable us to infer the life style of the car owners. The results can further be used to guide marketing towards car owners. Conventionally, this kind of study is carried out by sending out questionnaires, which is limited in scale and diversity. To solve this problem, we propose a fully automatic method to carry out this study. Our study is based on publicly available surveillance camera data. To make the results reliable, we only use the high resolution cameras (i.e. resolution greater than $1280 \times 720$). Images from the public cameras are downloaded every minute. After obtaining 50,000 images, we apply faster R-CNN (region-based convoluntional neural network) to detect the cars in the downloaded images and a fine-tuned VGG16 model is used to recognize the car makes. Based on the recognition results, we present a data-driven analysis on the relationship between car makes and their appearing times, with implications on lifestyles

Designing for Fast and Slow Circular Fashion Systems: Exploring Strategies for Multiple and Extended Product Cycles

Abstract: This paper reviews work conducted by practiced-based textile design researchers based at the University of the Arts London (UAL) who were part of the multi-disciplinary, Swedish-based Mistra Future Fashion research consortium between June 2011 – May 2015. The objective of the consortium was to research opportunities to advance a more sustainable, yet still profitable, fashion industry. The final stage of the project involved developing practice-based approaches through physical exhibition prototypes, which formed the basis of the project’s online exhibition, The Textile Toolbox (Earley & Goldsworthy, 2014). Here we discuss two of these design prototypes which both explored ‘designing for cyclability’ as a proactive approach to improving the retention of material value within ‘circular fashion systems’. Designing in order to enable fully joined up cycles of material use is the ultimate aim for both approaches, but this ‘speed’ of cycle creates very different challenges on which to make informed and appropriate design choices. The two approaches are deliberately extreme opposites, with ‘short-life’ closed-loop garments explored as complementary to ‘long-life’ user engagement strategies. Both can ultimately be argued to have an ‘extending’ affect on materials in the value-chain; one by keeping products in use over multiple cycles in perpetuity, the other by extending the single use cycle of a product over time. By exploring this polarisation of ‘speeds and needs’ we aim to gain insights into creating an effective circular materials economy, which acknowledges the complex nature of our current and emerging fashion system