Al-Robotics team: A cooperative multi-unmanned aerial vehicle approach for the Mohamed Bin Zayed International Robotic Challenge

The Al-Robotics team was selected as one of the 25 finalist teams out of 143 applications received to participate in the first edition of the Mohamed Bin Zayed International Robotic Challenge (MBZIRC), held in 2017. In particular, one of the competition Challenges offered us the opportunity to develop a cooperative approach with multiple unmanned aerial vehicles (UAVs) searching, picking up, and dropping static and moving objects. This paper presents the approach that our team Al-Robotics followed to address that Challenge 3 of the MBZIRC. First, we overview the overall architecture of the system, with the different modules involved. Second, we describe the procedure that we followed to design the aerial platforms, as well as all their onboard components. Then, we explain the techniques that we used to develop the software functionalities of the system. Finally, we discuss our experimental results and the lessons that we learned before and during the competition. The cooperative approach was validated with fully autonomous missions in experiments previous to the actual competition. We also analyze the results that we obtained during the competition trials.Unión Europea H2020 73166

Dependability checking with StoCharts: Is train radio reliable enough for trains?

Performance, dependability and quality of service (QoS) are prime aspects of the UML modelling domain. To capture these aspects effectively in the design phase, we have recently proposed STOCHARTS, a conservative extension of UML statechart diagrams. In this paper, we apply the STOCHART formalism to a safety critical design problem. We model a part of the European Train Control System specification, focusing on the risks of wireless communication failures in future high-speed cross-European trains. Stochastic model checking with the model checker PROVER enables us to derive constraints under which the central quality requirements are satisfied by the STOCHART model. The paper illustrates the flexibility and maturity of STOCHARTS to model real problems in safety critical system design

Using simulation studies to evaluate statistical methods

Simulation studies are computer experiments that involve creating data by pseudorandom sampling. The key strength of simulation studies is the ability to understand the behaviour of statistical methods because some 'truth' (usually some parameter/s of interest) is known from the process of generating the data. This allows us to consider properties of methods, such as bias. While widely used, simulation studies are often poorly designed, analysed and reported. This tutorial outlines the rationale for using simulation studies and offers guidance for design, execution, analysis, reporting and presentation. In particular, this tutorial provides: a structured approach for planning and reporting simulation studies, which involves defining aims, data-generating mechanisms, estimands, methods and performance measures ('ADEMP'); coherent terminology for simulation studies; guidance on coding simulation studies; a critical discussion of key performance measures and their estimation; guidance on structuring tabular and graphical presentation of results; and new graphical presentations. With a view to describing recent practice, we review 100 articles taken from Volume 34 of Statistics in Medicine that included at least one simulation study and identify areas for improvement.Comment: 31 pages, 9 figures (2 in appendix), 8 tables (1 in appendix

Energy Demand Response for High-Performance Computing Systems

The growing computational demand of scientific applications has greatly motivated the development of large-scale high-performance computing (HPC) systems in the past decade. To accommodate the increasing demand of applications, HPC systems have been going through dramatic architectural changes (e.g., introduction of many-core and multi-core systems, rapid growth of complex interconnection network for efficient communication between thousands of nodes), as well as significant increase in size (e.g., modern supercomputers consist of hundreds of thousands of nodes). With such changes in architecture and size, the energy consumption by these systems has increased significantly. With the advent of exascale supercomputers in the next few years, power consumption of the HPC systems will surely increase; some systems may even consume hundreds of megawatts of electricity. Demand response programs are designed to help the energy service providers to stabilize the power system by reducing the energy consumption of participating systems during the time periods of high demand power usage or temporary shortage in power supply. This dissertation focuses on developing energy-efficient demand-response models and algorithms to enable HPC system\u27s demand response participation. In the first part, we present interconnection network models for performance prediction of large-scale HPC applications. They are based on interconnected topologies widely used in HPC systems: dragonfly, torus, and fat-tree. Our interconnect models are fully integrated with an implementation of message-passing interface (MPI) that can mimic most of its functions with packet-level accuracy. Extensive experiments show that our integrated models provide good accuracy for predicting the network behavior, while at the same time allowing for good parallel scaling performance. In the second part, we present an energy-efficient demand-response model to reduce HPC systems\u27 energy consumption during demand response periods. We propose HPC job scheduling and resource provisioning schemes to enable HPC system\u27s emergency demand response participation. In the final part, we propose an economic demand-response model to allow both HPC operator and HPC users to jointly reduce HPC system\u27s energy cost. Our proposed model allows the participation of HPC systems in economic demand-response programs through a contract-based rewarding scheme that can incentivize HPC users to participate in demand response

Handling of realistic missing data scenarios in clinical trials using machine learning techniques

Missing data problem is a common challenge when designing and analyzing clinical trials, which are the data that are needed for the main analyses but are not collected. If the missing data are not properly imputed/handled, they may cause following issues: reduce the statistical power of the important analysis; they may bias/ confound the treatment effect estimation; they may cause an underestimation of the variability in target variable. Three different types of missingness are defined in Rubin’s 1976 paper. (1) MCAR (missing completely at random): when data are MCAR, “the probability of missingness does not depend on observed or unobserved measurements”, for example, subjects who dropout from the trial due to the reasons that are not related to their health status. (2) MAR (missing at random): when data are MAR, “the probability of missingness depends only on observed measurements conditional on the covariates in the model”, for example, younger subjects (those who don’t think it is necessary to measure their blood pressure as they consider themselves healthier) may more likely to have missing blood pressure. (3) MNAR (missing not at random): when data are MNAR, “the probability of missingness depends on unobserved measurements”, for example, subjects leave the trial because of “lack of efficacy” (i.e., they are not convinced by effec-tiveness of the study drug and hence dropout from the trial). Although all three types of missing data are well defined, it is very difficult to determine the association between missing data and unobserved outcomes in the real-world data; in other words, it is very difficult to justify the MAR assumption in any realistic situation. As EMA suggested in 2010, a combined strategy can be used, e.g., treat the discontinu-ations due to “lack of efficacy” as MNAR data, and treat the discontinuations due to “lost to follow-up” as MAR data. Many statistical methods have been developed to handle missing data under the prerequisite assumption of either MNAR or MAR. However, in the real world, missing data are often mixed with different types of missing mechanisms. This violates the basic assumptions for missing data (i.e., either MNAR or MAR), which leads to a degradation in the processing performance of these methods (Enders, 2010). To handle the missing data problem in reallife situations (e.g., MNAR and MAR mixed together in the same dataset), we propose a missing data prediction framework that are based on machine learning techniques. As Breiman pointed out in his 2001 paper, in the statistical (ma-chine) learning exercise, “the goal is not interpretability, but accurate information”. Along this line of thought, our methods handle MNAR by focusing on (giving more sample weights to) the missing part, meanwhile, and also to handle the MAR data by looking for precise individual (subject-level) information. The problem of MNAR is seen as an imbalanced machine learning exercise, i.e., to oversample the minority cases to compen-sate for the data that are MNAR in certain area