Don't Repeat Yourself: Seamless Execution and Analysis of Extensive Network Experiments

This paper presents MACI, the first bespoke framework for the management, the scalable execution, and the interactive analysis of a large number of network experiments. Driven by the desire to avoid repetitive implementation of just a few scripts for the execution and analysis of experiments, MACI emerged as a generic framework for network experiments that significantly increases efficiency and ensures reproducibility. To this end, MACI incorporates and integrates established simulators and analysis tools to foster rapid but systematic network experiments. We found MACI indispensable in all phases of the research and development process of various communication systems, such as i) an extensive DASH video streaming study, ii) the systematic development and improvement of Multipath TCP schedulers, and iii) research on a distributed topology graph pattern matching algorithm. With this work, we make MACI publicly available to the research community to advance efficient and reproducible network experiments

Formalising the Continuous/Discrete Modeling Step

Formally capturing the transition from a continuous model to a discrete model is investigated using model based refinement techniques. A very simple model for stopping (eg. of a train) is developed in both the continuous and discrete domains. The difference between the two is quantified using generic results from ODE theory, and these estimates can be compared with the exact solutions. Such results do not fit well into a conventional model based refinement framework; however they can be accommodated into a model based retrenchment. The retrenchment is described, and the way it can interface to refinement development on both the continuous and discrete sides is outlined. The approach is compared to what can be achieved using hybrid systems techniques.Comment: In Proceedings Refine 2011, arXiv:1106.348

The Impact of Early Positive Results on a Mathematics and Science Partnership: The Experience of the Institute for Chemistry Literacy Through Computational Science

After one year of implementation, the Institute for Chemistry Literacy through Computational Science, an NSF Mathematics and Science Partnership Institute Project led by the University of Illinois at Urbana-Champaign’s Department of Chemistry, College of Medicine, and National Center for Supercomputing Applications, experienced statistically significant gains in chemistry content knowledge among students of the rural high school teachers participating in its intensive, year-round professional development course, compared to a control group. The project utilizes a two-cohort, delayed-treatment, random control trial, quasi-experimental research design with the second cohort entering treatment one year following the first. The three-year treatment includes intensive two-week summer institutes, occasional school year workshops and year-round, on-line collaborative lesson development, resource sharing, and expert support. The means of student pre-test scores for Cohort I (η=963) and Cohort II (η=862) teachers were not significantly different. The mean gain (difference between pre-test and post-test scores) after seven months in the classroom for Cohort I was 9.8 percentage points, compared to 6.7 percentage points for Cohort II. This statistically significant difference (p\u3c.001) represented an effect size of .25 standard deviation units, and indicated unusually early confirmation of treatment effects. When post-tests were compared, Cohort I students scored significantly higher than Cohort II and supported the gain score differences. The impact of these results on treatment and research plans is discussed. concentrating on the effect of lessening rural teachers’ isolation and increasing access to tools to facilitate learning

Reduced basis method for source mask optimization

Image modeling and simulation are critical to extending the limits of leading edge lithography technologies used for IC making. Simultaneous source mask optimization (SMO) has become an important objective in the field of computational lithography. SMO is considered essential to extending immersion lithography beyond the 45nm node. However, SMO is computationally extremely challenging and time-consuming. The key challenges are due to run time vs. accuracy tradeoffs of the imaging models used for the computational lithography. We present a new technique to be incorporated in the SMO flow. This new approach is based on the reduced basis method (RBM) applied to the simulation of light transmission through the lithography masks. It provides a rigorous approximation to the exact lithographical problem, based on fully vectorial Maxwell's equations. Using the reduced basis method, the optimization process is divided into an offline and an online steps. In the offline step, a RBM model with variable geometrical parameters is built self-adaptively and using a Finite Element (FEM) based solver. In the online step, the RBM model can be solved very fast for arbitrary illumination and geometrical parameters, such as dimensions of OPC features, line widths, etc. This approach dramatically reduces computational costs of the optimization procedure while providing accuracy superior to the approaches involving simplified mask models. RBM furthermore provides rigorous error estimators, which assure the quality and reliability of the reduced basis solutions. We apply the reduced basis method to a 3D SMO example. We quantify performance, computational costs and accuracy of our method.Comment: BACUS Photomask Technology 201

The integration of new technologies : the stakes of knowledge

In order to remain competitive in an increasingly competitive international context, French companies are forced to follow one or more of various possible routes: relocating some of the activities, optimizing the design and / or production process, or innovate technologically. When they choose to develop new technologies, it is advisable to seek outside expertise in different areas. Thus they must exchange and create knowledge in partnership with other companies. But in order to control and integrate this future technology, we support that the acquisition and the capitalization of the technical training, during the process of innovation, are of primary importance. This article demonstrates that the construction of this knowledge base can be achieved only by formalizing close and rigorous collaboration. To do this, we propose a model of the collaborative process, meant for the leaders of innovative projects to support design.Cifr

Automated Verification of Design Patterns with LePUS3

Specification and [visual] modelling languages are expected to combine strong abstraction mechanisms with rigour, scalability, and parsimony. LePUS3 is a visual, object-oriented design description language axiomatized in a decidable subset of the first-order predicate logic. We demonstrate how LePUS3 is used to formally specify a structural design pattern and prove (‗verify‘) whether any JavaTM 1.4 program satisfies that specification. We also show how LePUS3 specifications (charts) are composed and how they are verified fully automatically in the Two-Tier Programming Toolkit

The Effects of the Louisiana Scholarship Program on Student Achievement After Two Years

The Louisiana Scholarship Program (LSP) is a statewide initiative offering publicly-funded vouchers to enroll in local private schools to students in low-performing schools with family income no greater than 250 percent of the poverty line. Initially established in 2008 as a pilot program in New Orleans, the LSP was expanded statewide in 2012. This paper examines the experimental effects of using an LSP scholarship to enroll in a private school on student achievement in the first two years following the program’s expansion. Our results indicate that the use of an LSP scholarship has negatively impacted both ELA and math achievement, although only the latter estimates are statistically significant. Moreover, we observe less negative effect estimates in the second year of the program