Oracle (Germany)

As software projects grow increasingly more complex, debloating gains traction. While static analyses yield a coarse over-approximation of reachable code, approaches based on dynamic execution traces risk program correctness. By allowing the developer to reconsider only a few methods and still achieve a significant reduction in code size, cut-based debloating can minimize the risk. In this paper, we propose the idea of finding small cuts in the rule graphs produced by static analysis. After introducing an analysis with suitable semantics, we discuss how to encode its rules into a directed hypergraph. We then present an algorithm for efficiently finding the most effective single cut in the graph. The execution time of the proposed operations allows for the deployment in interactive tools. Finally, we show that our graph model is able to expose methods worthwhile to reconsider.

This paper presents the MLAB project, a research and development activity funded by ESA General Support Technology Programme under the lead of Airbus Defence and Space GmbH, with the goal of developing a machine learning application benchmark for space applications. First, the need for a benchmark dedicated to machine learning applications in spacecraft is explained, and examples of applications are described including their design challenges. Then the benchmark design is presented, including the rules of the metrics, guidelines and scenarios for references. These scenarios include a description of the reference workloads that have been selected during the activity as representative for spacecraft applications. Lastly, the submission concept is introduced.

BackgroundIntroduction of new tobacco products in the United States, including those that may be lower on the risk continuum than traditional combustible cigarettes, requires premarket authorization by the US Food and Drug Administration and information on the potential impact of the products on consumer behaviors. Efficient recruitment and data capture processes are needed to collect relevant information in a near-to-real-world environment.ObjectiveThe aim of this pilot study was to develop and test a protocol for an actual use study of a new tobacco product. The product included in this study was a commercially available oral nicotine pouch. Through the process of study design and execution, learnings were garnered to inform the design, execution, analysis, and report writing of future full-scale actual use studies with tobacco products.MethodsA small sample (n=100) of healthy adult daily smokers of 7 or more cigarettes per day were recruited to participate in an 8-week prospective observational study conducted at 4 geographically dispersed sites in the United States. A smartphone-based customized electronic diary (eDiary) was employed to capture daily tobacco product use, including 1 week of baseline smoking and 6 weeks during which participants were provided with oral nicotine pouches for use as desired.ResultsOnline screening procedures with follow-up telephone interviews and on-site enrollment were successfully implemented. Of 100 participants, 97 completed the study, with more than half (59/99, 60%) identifying as dual- or poly-users of cigarettes and other types of tobacco products at baseline. There was more than 90% (91-93/99, 92%-94%) compliance with daily eDiary reporting, and the majority (92/99, 93%) of participants expressed satisfaction with the study processes. Product use data from the eDiary indicated that after an initial period of trial use, pouches per day increased among those continuing to use the products, while per day average cigarette consumption decreased for 82% (79/97) of all study participants. At the end of the week 6, 16% (15/97) of participants had reduced their cigarette consumption by more than half.ConclusionsThe design of this study, including recruiting, enrollment, eDiary use, and oversight, was successfully implemented through the application of a detailed protocol, a user-friendly eDiary, electronically administered questionnaires, and remote monitoring procedures. High-resolution information was obtained on prospective changes in tobacco product use patterns in the context of availability of a new tobacco product. Future, larger actual use studies will provide important evidence supporting the role that alternatives to combustible cigarettes may play in smoking reduction and/or cessation and lowering the population health burden of tobacco and nicotine-containing products.

We present our experience with the modernization on the GR-MHD code BHAC, aimed at improving its novel hybrid (MPI+OpenMP) parallelization scheme. In doing so, we showcase the use of performance profiling tools usable on x86 (Intel-based) architectures. Our performance characterization and threading analysis provided guidance in improving the concurrency and thus the efficiency of the OpenMP parallel regions. We assess scaling and communication patterns in order to identify and alleviate MPI bottlenecks, with both runtime switches and precise code interventions. The performance of optimized version of BHAC improved by $\sim28\%$, making it viable for scaling on several hundreds of supercomputer nodes. We finally test whether porting such optimizations to different hardware is likewise beneficial on the new architecture by running on ARM A64FX vector nodes.

Language implementation frameworks aim to provide everything that is needed to build interpreters, simplify the process by making certain design decisions in advance, and suggest implementation strategies to virtual machine creators. Truffle, the language implementation framework for the GraalVM, is designed for building Abstract Syntax Tree interpreters and the process of doing so is well documented. However, although less documented, Truffle can also be used to implement bytecode interpreters. This approach requires additional hints to be passed into the compiler to gain good performance.In this paper, we compare two Truffle interpreters for Squeak/Smalltalk, one using an ast implementation approach and the other executing bytecodes. While both run at roughly three times the speed of the standard Squeak/Smalltalk virtual machine, both represent different trade-offs in implementation strategies for interpreters in Truffle. We compare these trade-offs and discuss the advantages and disadvantages of the different approaches.

Some practical experience is summarized here. Some statistical information on the categories of problems which have occurred in practice is also presented.

Das ist das erste der drei Kapitel, in denen die formale Methode beschrieben wird. In diesem Kapitel formulieren wir das Ziel, das Prinzip, die Methode, das Hauptkriterium, die Handlungsweise und die Richtlinie des formalen SQL-Tunings.

In recent years, significant achievements have been made on handling qualitative spatial relations in the field of qualitative spatial reasoning. These achievements can be utilized to bridge the gap between geometries and semantics of real-world objects. With this purpose we introduce the Semantic Spatial Reasoning conceptual framework for reasoning with information of mixed types: qualitative-quantitative spatial and information described with the help of the Semantic Web technologies. The objective of this framework is not to be a particular reasoning algorithm but a conceptual decomposition suitable for showing benefits of the combined reasoning approach, forecasting practical applications and giving a clue for an implementation. Modular structure of the framework makes it useful to model various tasks in areas, such as, GIS, cognitive vision, computer-aided design, data integration.

Wir haben bereits ein paar Features von Oracle 11g besprochen (beispielsweise SQL Monitoring im Abschn. 6.2 oder erweiterte Optimizer Statistics im Abschn. 7.1). In diesem Kapitel besprechen wir weitere Features von Oracle 11g:

Signal detection in drug safety research traditionally relies on the use of spontaneous reporting (SR) data. Recently, the interest in electronic health care data for signal detection purposes grows, and data mining techniques developed for SR data are now being modified and used to highlight possible safety risks in such longitudinal data. We present simple frequentistic methods such as the reporting odds ratio (ROR) and the proportional reporting ratio (PRR), the widely-used Bayesian Gamma-Poisson shrinkage algorithm (GPS) and extensions for the use on longitudinal healthcare data. In an exemplary application to German claims data, we aim to recapture known signals for intracerebral hemorrhage under exposure to phenprocoumon and to compare the results obtained by the longitudinal Bayesian shrinkage algorithm with risk estimates from classical pharmacoepidemiological studies. We also highlight the still unsolved problem of adequate controlling for confounding in automated database studies.