Prediction and Optimal Scheduling of Advertisements in Linear Television

Advertising is a crucial component of marketing and an important way for companies to raise awareness of goods and services in the marketplace. Advertising campaigns are designed to convey a marketing image or message to an audience of potential consumers and television commercials can be an effective way of transmitting these messages to a large audience. In order to meet the requirements for a typical advertising order, television content providers must provide advertisers with a predetermined number of impressions in the target demographic. However, because the number of impressions for a given program is not known a priori and because there are a limited number of time slots available for commercials, scheduling advertisements efficiently can be a challenging computational problem. In this case study, we compare a variety of methods for estimating future viewership patterns in a target demographic from past data. We also present a method for using those predictions to generate an optimal advertising schedule that satisfies campaign requirements while maximizing advertising revenue

Prediction and Optimal Scheduling of Advertisements in Linear Television

Advertising is a crucial component of marketing and an important way for companies to raise awareness of goods and services in the marketplace. Advertising campaigns are designed to convey a marketing image or message to an audience of potential consumers and television commercials can be an effective way of transmitting these messages to a large audience. In order to meet the requirements for a typical advertising order, television content providers must provide advertisers with a predetermined number of impressions in the target demographic. However, because the number of impressions for a given program is not known a priori and because there are a limited number of time slots available for commercials, scheduling advertisements efficiently can be a challenging computational problem. In this case study, we compare a variety of methods for estimating future viewership patterns in a target demographic from past data. We also present a method for using those predictions to generate an optimal advertising schedule that satisfies campaign requirements while maximizing advertising revenue

Machine Learning in High Energy Physics Community White Paper

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit

The Youngest Victims: Children and Youth Affected by War

In 1989, the United Nation Convention on the Rights of the Child declared, “[state parties] shall take all feasible measures to ensure protection and care of children who are affected by an armed conflict.” In addition to attempting to secure the welfare of children in armed conflict, the Convention went on to ban the recruitment and deployment of children during armed conflict. Despite the vast majority of sovereign nations signing and ratifying this agreement, this treaty, unfortunately, has not prevented children and youth from witnessing, becoming victims of, or participating in political, ethnic, religious, and cultural violence across the past three decades. This chapter offers an “ecological perspective” on the psychosocial consequences of exposure to the trauma of war-related violence and social disruption

Modern Industrial Economics and Competition Policy: Open Problems and Possible Limits

Naturally, competition policy is based on competition economics made applicable in terms of law and its enforcement. Within the different branches of competition economics, modern industrial economics, or more precisely gametheoretic oligopoly theory, has become the dominating paradigm both in the U.S. (since the 1990s Post-Chicago movement) and in the EU (so-called more economic approach in the 2000s). This contribution reviews the state of the art in antitrust-oriented modern industrial economics and, in particular, critically discusses open questions and possible limits of basing antitrust on modern industrial economics. In doing so, it provides some hints how to escape current enforcement problems in industrial economics-based competition policy on both sides of the Atlantic. In particular, the paper advocates a change of the way modern industrial economics is used in competition policy: instead of more and more case-by-cases analyses, the insights from modern industrial economics should be used to design better competition rules

Thermodynamic Characterization of a Direct Water Cooled Server Rack Running Synthetic and Real High Performance Computing Work Loads

High performance computing server racks are being engineered to contain significantly more processing capability within the same computer room footprint year after year. The processor density within a single rack is becoming high enough that traditional, inefficient air-cooling of servers is inadequate to sustain HPC workloads. Experiments that characterize the performance of a direct water-cooled server rack in an operating HPC facility are described in this paper. Performance of the rack is reported for a range of cooling water inlet temperatures, flow rates and workloads that include actual and worst-case synthetic benchmarks. Power and temperature measurements of all processors and memory components in the rack were made while extended benchmark tests were conducted throughout the range of cooling variables allowed within an operational HPC facility. Synthetic benchmark results were compared with those obtained on a single server of the same design that had been characterized thermodynamically. Neither actual nor synthetic benchmark performances were affected during the course of the experiments, varying less than 0.13 percent. Power consumption change in the rack was minimal for the entire excursion of coolant temperatures and flow rates. Establishing the characteristics of such a highly energy efficient server rack in situ is critical to determine how the technology might be integrated into an existing heterogeneous, hybrid cooled computing facility — i.e., a facility that includes some servers that are air cooled as well as some that are direct water cooled.</jats:p