MODELING STATISTICALLY SIGNIFICANT PROPENSITY CONTROLS

A mechanism for modeling statistically significant propensity controls in survey lift studies is disclosed. The proposed mechanism establishes a statistically significant control model for organic viewers in order to measure attitudinal shifts and lift from the viewing population. On a high level, audience overlap is measured to find channels that are similar to the channels in an organic video campaign. Active subscribers of the identified similar channels are then used as the non-exposed group, after filtering out those who watched the organic videos. The resulting channels that share viewer audiences are filtered by channel topicality (e.g., electronics reviews, beauty tips, etc.) and channel size (subscribers within a standard deviation). Viewers are filtered by demographic, technographic and psychographic traits to align with that of the exposed groups

Predicting Content Views Using Finite Integrals

Video hosting and sharing services enable creators and advertisers to create campaigns that engage viewers. To price the advertisements, and to give advertisers on the campaign an idea of the popularity of the content, the viewership is predicted. Both under- and over-prediction of views are associated with penalties, respectively of wasted inventory and capacity crunches. View estimations based on channel average suffer from sample bias and invisible trends. This disclosure describes techniques of in-flight view prediction, e.g., predictions of views done after the launch of a campaign for the remaining days of a campaign. The predictions of the total views on a line-up of in-flight videos are based on the distributions of prior view history. The described predictor delivers continuously improving predictions for live videos, and enables determination of whether a campaign is meeting view goals. It thereby enables real-time fine-tuning of inventory and capacity for the remaining days of the campaign

Distortion of trichome morphology by the hairless mutation of tomato affects leaf surface chemistry

Trichomes are specialized epidermal structures that function as physical and chemical deterrents against arthropod herbivores. Aerial tissues of cultivated tomato (Solanum lycopersicum) are populated by several morphologically distinct trichome types, the most abundant of which is the type VI glandular trichome that produces various specialized metabolites. Here, the effect of the hairless (hl) mutation on trichome density and morphology, chemical composition, and resistance to a natural insect herbivore of tomato was investigated. The results show that the major effect of hl on pubescence results from structural distortion (bending and swelling) of all trichome types in aerial tissues. Leaf surface extracts and isolated type VI glands from hl plants contained wild-type levels of monoterpenes, glycoalkaloids, and acyl sugars, but were deficient in sesquiterpene and polyphenolic compounds implicated in anti-insect defence. No-choice bioassays showed that hl plants are compromised in resistance to the specialist herbivore Manduca sexta. These results establish a link between the morphology and chemical composition of glandular trichomes in cultivated tomato, and show that hl-mediated changes in these leaf surface traits correlate with decreased resistance to insect herbivory

Evaluating MapReduce for multi-core and multiprocessor systems

This paper evaluates the suitability of the MapReduce model for multi-core and multi-processor systems. MapReduce was created by Google for application development on data-centers with thousands of servers. It allows programmers to write functional-style code that is automatically parallelized and scheduled in a distributed system. We describe Phoenix, an implementation of MapReduce for shared-memory systems that includes a programming API and an efficient runtime system. The Phoenix runtime automatically manages thread creation, dynamic task scheduling, data partitioning, and fault tolerance across processor nodes. We study Phoenix with multi-core and symmetric multiprocessor systems and evaluate its performance potential and error recovery features. We also compare MapReduce code to code written in lower-level APIs such as P-threads. Overall, we establish that, given a careful implementation, MapReduce is a promising model for scalable performance on shared-memory systems with simple parallel code.