Control Using Beckhoff Distributed Rail Systems at the European XFEL

The European XFEL project is a 4th generation light source producing spatially coherent 80fs short photon x-ray pulses with a peak brilliance of 1032-1034 photons/s/mm2/mrad2/0.1% BW in the energy range from 0.26 to 24 keV at an electron beam energy 14 GeV. Six experiment stations will start data taking in fall 2015. In order to provide a simple, homogeneous solution, the DAQ and control systems group at the European XFEL are standardizing on COTS control hardware for use in experiment and photon beam line tunnels. A common factor within this standardization requirement is the integration with the Karabo software framework of Beckhoff TwinCAT 2.11 or TwinCAT3 PLCs and EtherCAT. The latter provides the high degree of reliability required and the desirable characteristics of real time capability, fast I/O channels, distributed flexible terminal topologies, and low cost per channel. In this contribution we describe how Beckhoff PLC and EtherCAT terminals will be used to control experiment and beam line systems. This allows a high degree of standardization for control and monitoring of systems

The Large Scale European XFEL Control System: Overview and Status of the Commissioning

The European XFEL is a 3.4 km long X-ray Free Electron Laser in the final construction and commissioning phase in Hamburg. It will produce 27000 bunches per second at 17.5 GeV. Early 2015 a first electron beam was produced in the RF-photo-injector and the commissioning of consecutive sections will follow during this and next year. The huge number and variety of devices for the accelerator, beam line, experiment, cryogenic and facility systems pose a challenging control task. Multiple systems, including industrial solutions, must be interfaced to each other. The large number of bunches requires a tight time synchronization (down to picoseconds) and high performance data acquisition systems. Fast feedbacks from front-ends, the DAQs and online analysis system with a seamless integration of controls are essential for the accelerator and the initially 6 experimental end stations. It turns out that the European XFEL will be the first installation exceeding 2500 FPGA components in the MicroTCA form factor and will run one of the largest PROFIBUS networks. Many subsystem prototypes are already successfully in operation. An overview and status of the XFEL control system will begiven

The Large Scale European XFEL Control System: Overview and Status of the Commissioning

The European XFEL is a 3.4 km long X-ray Free Electron Laser in the final construction and commissioning phase in Hamburg. It will produce 27000 bunches per second at 17.5 GeV. Early 2015 a first electron beam was produced in the RF-photo-injector and the commissioning of consecutive sections will follow during this and next year. The huge number and variety of devices for the accelerator, beam line, experiment,cryogenic and facility systems pose a challenging control task. Multiple systems, including industrial solutions, must be interfaced to each other. The large number of bunches requires a tight time synchronization (down to picoseconds) and high performance data acquisitionsystems. Fast feedbacks from front-ends, the DAQs and online analysis system with a seamless integration of controls are essential for the accelerator and the initially 6 experimental end stations. It turns out that the European XFEL will be the first installation exceeding 2500 FPGAcomponents in the MicroTCA form factor and will run one of the largest PROFIBUS networks. Many subsystem prototypes are already successfully in operation. An overview and status of the XFEL control system will be given

The Large Scale European XFEL Control System: Overview and Status of the Commissioning

The European XFEL is a 3.4km long X-ray Free Electron Laser in the final construction and commissioning phase in Hamburg. It will produce 27000 bunches per second at 17.5GeV. Early 2015 a first electron beam was produced in the RF-photo-injector and the commissioning of consecutive sections is following during this and next year. The huge number and variety of devices for the accelerator, beam line, experiment, cryogenic and facility systems pose a challenging control task. Multiple systems, including industrial solutions, must be interfaced to each other. The high number of bunches requires a tight time synchronization (down to picoseconds) and high performance data acquisition systems. Fast feedbacks from front-ends, the DAQs and online analysis system with a seamless integration of controls are essential for the accelerator and the initially 6 experimental end stations. It turns out that the European XFEL will be the first installation exceeding 2500 FPGA components in the MicroTCA form factor and will run one of the largest PROFIBUS networks. Many subsystem prototypes are already successfully in operation. An overview and status of the XFEL control system will be given

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc