Ultrafast processing of pixel detector data with machine learning frameworks

Modern photon science performed at high repetition rate free-electron laser (FEL) facilities and beyond relies on 2D pixel detectors operating at increasing frequencies (towards 100 kHz at LCLS-II) and producing rapidly increasing amounts of data (towards TB/s). This data must be rapidly stored for offline analysis and summarized in real time. While at LCLS all raw data has been stored, at LCLS-II this would lead to a prohibitive cost; instead, enabling real time processing of pixel detector raw data allows reducing the size and cost of online processing, offline processing and storage by orders of magnitude while preserving full photon information, by taking advantage of the compressibility of sparse data typical for LCLS-II applications. We investigated if recent developments in machine learning are useful in data processing for high speed pixel detectors and found that typical deep learning models and autoencoder architectures failed to yield useful noise reduction while preserving full photon information, presumably because of the very different statistics and feature sets between computer vision and radiation imaging. However, we redesigned in Tensorflow mathematically equivalent versions of the state-of-the-art, "classical" algorithms used at LCLS. The novel Tensorflow models resulted in elegant, compact and hardware agnostic code, gaining 1 to 2 orders of magnitude faster processing on an inexpensive consumer GPU, reducing by 3 orders of magnitude the projected cost of online analysis at LCLS-II. Computer vision a decade ago was dominated by hand-crafted filters; their structure inspired the deep learning revolution resulting in modern deep convolutional networks; similarly, our novel Tensorflow filters provide inspiration for designing future deep learning architectures for ultrafast and efficient processing and classification of pixel detector images at FEL facilities.Comment: 9 pages, 9 figure

Taking ELF off the shelf: Developing HE students' speaking skills through a focus on English as a lingua franca

This paper explores how principles derived from English as a lingua franca (ELF) research (e.g. accommodation, strategic competence) can provide insights into the speaking demands of group work in Anglophone EMI settings which includes native speakers as well as non-native speakers. The paper maps data gathered through interviews with first year undergraduate students against Mercer et al.’s (2017) oracy framework. It shows that students draw on a combination of linguistic, cognitive, physical and social & emotional skills, many of which align with ELF principles. However, current frameworks of support for speaking demands in HE (EAP and academic skills) lack focus on dialogic speaking, pay little heed to ELF findings and cater for native speakers and non-native speakers separately despite their needs being similar. The paper argues that a focus on ELF can contribute to the development of speaking support which sits at the centre of students’ academic journey and encourages better interactions between native and non-native speakers

Walking and vision in blowflies

In this thesis eye movements of walking and flying blowflies are studied in detail. Eye movements of small insects are difficult to measure with a spatio-temporal accuracy comparable with that of the eyes, and current video techniques do not allow an accurate reconstruction of the visual input. In this thesis an existing technique is used, based on search-coils in magnetic fields, which can measure eye movements with a spatio-temporal accuracy comparable to that of the eyes. ... Zie: Samenvatting

Medipix3RX: Characterizing the Medipix3 Redesign With Synchrotron Radiation

The Medipix3RX is the latest version of the Medipix3 photon counting ASICs, which implements two new operational modes, with respect to the Medipix2 ASIC, aimed at eliminating charge shared events (referred to as Charge Summing Mode (CSM)) and at providing spectroscopic information (referred to as Colour Mode (CM)). The Medipix3RX is a redesign of the Medipix3v0 ASIC and corrects for the underperformance of CSM features observed in the previous version. This paper presents the results from synchrotron X-rays tests to evaluate the Medipix3RX ASIC performance. The newly implemented CSM algorithm eliminates the charge sharing effect at the same time as allocating the event to the readout pixel corresponding to the sensor pixel where the X-ray photon impinged. The new pixel trimming circuit led to a reduced dispersion between pixels. Further results of the linearity for all the gain modes, energy resolution and pixel uniformity are also presented