Individual Fairness in Pipelines

It is well understood that a system built from individually fair components may not itself be individually fair. In this work, we investigate individual fairness under pipeline composition. Pipelines differ from ordinary sequential or repeated composition in that individuals may drop out at any stage, and classification in subsequent stages may depend on the remaining "cohort" of individuals. As an example, a company might hire a team for a new project and at a later point promote the highest performer on the team. Unlike other repeated classification settings, where the degree of unfairness degrades gracefully over multiple fair steps, the degree of unfairness in pipelines can be arbitrary, even in a pipeline with just two stages. Guided by a panoply of real-world examples, we provide a rigorous framework for evaluating different types of fairness guarantees for pipelines. We show that na\"{i}ve auditing is unable to uncover systematic unfairness and that, in order to ensure fairness, some form of dependence must exist between the design of algorithms at different stages in the pipeline. Finally, we provide constructions that permit flexibility at later stages, meaning that there is no need to lock in the entire pipeline at the time that the early stage is constructed

Managing water loss for sustainability of rural water supply

Water supply for rural areas flows through pipelines from many sources such as underground water and reservoirs. Due to factors such as human activities, geographic changes and natural hazards, pipelines are exposed to the ground, crack, leak and burst. Water flows out from the pipelines before reaching to the consumers is considered as water loss and this is a major component in non-revenue water. This paper highlights a framework and a work procedure for non-revenue water analysis. Based on this analysis, a comprehensive and flexible web based prototype has been developed to manage the water loss. Functions that have been developed are data recording,non-revenue water estimation and report generation. This prototype can be used by the water authority for assisting decision making and planning

Time-Shared Execution of Realtime Computer Vision Pipelines by Dynamic Partial Reconfiguration

This paper presents an FPGA runtime framework that demonstrates the feasibility of using dynamic partial reconfiguration (DPR) for time-sharing an FPGA by multiple realtime computer vision pipelines. The presented time-sharing runtime framework manages an FPGA fabric that can be round-robin time-shared by different pipelines at the time scale of individual frames. In this new use-case, the challenge is to achieve useful performance despite high reconfiguration time. The paper describes the basic runtime support as well as four optimizations necessary to achieve realtime performance given the limitations of DPR on today's FPGAs. The paper provides a characterization of a working runtime framework prototype on a Xilinx ZC706 development board. The paper also reports the performance of realtime computer vision pipelines when time-shared

A First Comparison Between LIGO and Virgo Inspiral Search Pipelines

This article reports on a project that is the first step the LIGO Scientific Collaboration and the Virgo Collaboration have taken to prepare for the mutual search for inspiral signals. The project involved comparing the analysis pipelines of the two collaborations on data sets prepared by both sides, containing simulated noise and injected events. The ability of the pipelines to detect the injected events was checked, and a first comparison of how the parameters of the events were recovered has been completed.Comment: GWDAW-9 proceeding

R&D Portfolios and Pharmaceutical Licensing

We examine how R&D portfolios of drug pipelines affect pharmaceutical licensing, controlling firm size, diversity, and competitors in R&D and product markets. The data collected comprises 329 license-outs and 434 license-ins closed by 54 Japanese pharmaceutical companies between 1997 and 2007. We pay special attention to stage-specific licensing by dividing the innovation process into an early stage and a late stage. Estimates from the fixed-effect GMM model reveal that drug pipelines significantly affect stage-specific licensing. Particularly, the state of drug pipelines is leveled off by license-outs at the early stage and license-ins at the late stage. Theoretical implications are also discussed.R&D portfolios, licensing, pharmaceutical industry, drug pipelines

Predicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering

Evaluating the computational reproducibility of data analysis pipelines has become a critical issue. It is, however, a cumbersome process for analyses that involve data from large populations of subjects, due to their computational and storage requirements. We present a method to predict the computational reproducibility of data analysis pipelines in large population studies. We formulate the problem as a collaborative filtering process, with constraints on the construction of the training set. We propose 6 different strategies to build the training set, which we evaluate on 2 datasets, a synthetic one modeling a population with a growing number of subject types, and a real one obtained with neuroinformatics pipelines. Results show that one sampling method, "Random File Numbers (Uniform)" is able to predict computational reproducibility with a good accuracy. We also analyze the relevance of including file and subject biases in the collaborative filtering model. We conclude that the proposed method is able to speedup reproducibility evaluations substantially, with a reduced accuracy loss