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Register a new userTune Evaluation From Phased BPM Turn-By-Turn Data
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In fast ramping synchrotrons like the Fermilab Booster the conventional methods of betatron tune evaluation from the turn-by-turn data may not work due to rapid changes of the tunes (sometimes in a course of a few dozens of turns) and a high level of noise. We propose a technique based on phasing of signals from a large number of BPMs which significantly increases the signal to noise ratio. Implementation of the method in the Fermilab Booster control system is described and some measurement results are presented.
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Recently there has been a huge interest in dialog systems. This interest has also been developed in the field of the medical domain where researchers are focusing on building a dialog system in the medical domain. This research is focused on the multi-turn dialog system trained on the multi-turn dialog data. It is difficult to gather a huge amount of multi-turn conversational data in the medical domain that is verified by professionals and can be trusted. However, there are several frequently asked questions (FAQs) or single-turn QA pairs that have information that is verified by the experts and can be used to build a multi-turn dialog system.
A dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation metrics should reflect the dynamics of such interaction. Existing automatic metrics are focused very much on the turn-level quality, while ignoring such dynamics. To this end, we propose DynaEval, a unified automatic evaluation framework which is not only capable of performing turn-level evaluation, but also holistically considers the quality of the entire dialogue. In DynaEval, the graph convolutional network (GCN) is adopted to model a dialogue in totality, where the graph nodes denote each individual utterance and the edges represent the dependency between pairs of utterances. A contrastive loss is then applied to distinguish well-formed dialogues from carefully constructed negative samples. Experiments show that DynaEval significantly outperforms the state-of-the-art dialogue coherence model, and correlates strongly with human judgements across multiple dialogue evaluation aspects at both turn and dialogue level.
The beam position monitor (BPM) system is of most importance in a light source. The capability of the BPM depends on the resolution of the system. The traditional standard deviation on the raw data method merely gives the upper limit of the resolution. Principal component analysis (PCA) had been introduced in the accelerator physics and it could be used to get rid of the actual signals. Beam related informations were extracted before the evaluation of the BPM performance. A series of studies had been made in Shanghai Synchrotron Radiation Facility (SSRF) and PCA was proved as an effective and robust method in the performance evaluations of our BPM system.
We report on studies of the loss maps for particles travelling from the end of the ThomXs linac along the transfer line to the end of the ring first turn in preparation of the machine commissioning. ThomX is a 50-MeV-electron accelerator prototype which will use Compton backscattering to generate a high flux of hard X-rays. The accelerator tracking code MadX is used to simulate electrons propagation and compute losses. These maps may be projected at any localisation along the bunch path or plotted along the bunch path. This information is particularly relevant at the locations of the monitoring devices (screens, position monitors,...) where loss predictions will be compared with measurements.
Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is of-ten the first step when conducting accelerator-based experiments. Currently used techniques for characterization, such as grid-like parameter sampling scans, become impractical when extended to higher dimensional input spaces, when complicated measurement constraints are present, or prior information is known about the beam response is scarce. In this work, we describe an adaptation of the popular Bayesian optimization algorithm, which enables a turn-key exploration algorithm that replaces parameter scans and minimizes prior information needed about the measurements behavior and associated measurement constraints. We experimentally demonstrate that our algorithm autonomously conducts an adaptive, multi-parameter exploration of input parameter space,while navigating a highly constrained, single-shot beam phase-space measurement. In addition to applications in accelerator-based scientific experiments, this algorithm addresses challenges shared by many scientific disciplines and is thus applicable to autonomously conducting experiments over a broad range of research topics.
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