Shape reconstruction from gradient data

We present a novel method for reconstructing the shape of an object from measured gradient data. A certain class of optical sensors does not measure the shape of an object, but its local slope. These sensors display several advantages, including high information efficiency, sensitivity, and robustness. For many applications, however, it is necessary to acquire the shape, which must be calculated from the slopes by numerical integration. Existing integration techniques show drawbacks that render them unusable in many cases. Our method is based on approximation employing radial basis functions. It can be applied to irregularly sampled, noisy, and incomplete data, and it reconstructs surfaces both locally and globally with high accuracy.Comment: 16 pages, 5 figures, zip-file, submitted to Applied Optic

Context-based Pricing for Revenue Optimization with Applications to the Airline Industry

Most airlines use dynamic pricing to optimize the price of their base economy product by maximizing the expected revenue. However, when it comes to pricing of premium products, airlines often uses a static price increments that are applied to the best available economy fare based on simple business rules for adjusting the price based on supply. In this paper, we present a suite of machine learning algorithms that take advantage of the rich booking session context available at the time of the booking to make its predictions. The challenge is to accurately predict bookings for new combinations of attributes by market and segment (departure time, length of stay, advance purchase, length of haul, …) while accounting for cross-product price effects in a scalable manner. To generate practical pricing policies, the approach accommodates a variety of real-world business requirements into the decision optimization problem. We present a scalable approach based on a novel path-based mixed-integer program (MIP) reformulation that can efficiently recover near-optimal pricing policies. We demonstrate the efficacy of our model with extensive experiments on synthetic and real-life data. Finally, we present an airline case study on deriving profitable prescriptive policies for premium cabin tickets based on easily interpretable pricing rules

Fatigue-aware Bandits for Dependent Click Models

As recommender systems send a massive amount of content to keep users engaged, users may experience fatigue which is contributed by 1) an overexposure to irrelevant content, 2) boredom from seeing too many similar recommendations. To address this problem, we consider an online learning setting where a platform learns a policy to recommend content that takes user fatigue into account. We propose an extension of the Dependent Click Model (DCM) to describe users' behavior. We stipulate that for each piece of content, its attractiveness to a user depends on its intrinsic relevance and a discount factor which measures how many similar contents have been shown. Users view the recommended content sequentially and click on the ones that they find attractive. Users may leave the platform at any time, and the probability of exiting is higher when they do not like the content. Based on user's feedback, the platform learns the relevance of the underlying content as well as the discounting effect due to content fatigue. We refer to this learning task as "fatigue-aware DCM Bandit" problem. We consider two learning scenarios depending on whether the discounting effect is known. For each scenario, we propose a learning algorithm which simultaneously explores and exploits, and characterize its regret bound

IGAS (Innovative GPS Antenna System) – A Novel GPS Antenna Concept for Spin-Stabilized Sounding Rockets

This paper addresses a novel GPS antenna system for spin-stabilized sounding rockets and launch vehicles. It describes the concept as well as the first realization of the newly developed system. Furthermore it presents the results of on-ground tests conducted on a turn-table with the system installed into a mock-up of a rocket section. The promising outcome of these tests justified the subsequent preparation of a flight experiment. In the second part of this paper, the results of the maiden flight of the IGAS system onboard a real sounding rocket, the Rexus-4 vehicle, are summarised and discussed. The qualification flight has demonstrated that the system, in general, performs well and even outperforms the traditionally used combination of tip and blade antennas. However, it has also been recognized, that further adaptations in the GPS receiver software

A Design Methodology for Kanban-Controlled Production Lines Using Queueing Networks And Genetic Algorithms

This paper addresses two fundamental design issues in kanban systems and presents an efficient heuristic method for designing such systems. An analytical technique for modellingkanban systems and a general-purpose genetic algorithm are integrated in a heuristic design methodology which evaluates the performance of kanban systems using alternative network partitions and allocations of kanbans. As we demonstrate, the heuristic method provides a useful procedure to evaluate the impact of design alternatives and can thus serve as a rough-cut decision support tool which assists managers in the planning of large-scale manufacturing systems

Business Performance Management System for CRM and Sales Execution 1

In 2004 the IBM Telesales organization launched a new customer segmentation process to improve profits, revenue growth and customer satisfaction. The challenges were to automatically monitor customer segment status to ensure results are in line with segment targets, and to automatically generate highquality predictive analytical models to improve customer segmentation rules and management over time. This paper describes a software solution that combines business performance management with data mining techniques to provide a powerful combination of performance monitoring and proactive customer management in support of the new telesales business processes. 1