SIRIS: a high resolution scanning infrared camera for examining paintings

The new SIRIS (Scanning InfraRed Imaging System) camera developed at the National Gallery in London allows highresolution images of paintings to be made in the near infrared region (900–1700 nm). Images of 5000 × 5000 pixels are made by moving a 320 × 256 pixel InGaAs array across the focal plane of the camera using two orthogonal translation stages. The great advantages of this camera over scanning infrared devices are its relative portability and that image acquisition is comparatively rapid – a full 5000 × 5000 pixel image can be made in around 20 minutes. The paper describes the development of the mechanical, optical and electronic components of the camera, including the design of a new lens. The software routines used to control image capture and to assemble the individual 320 × 256 pixel frames into a seamless mosaic image are also mentioned. The optics of the SIRIS camera have been designed so that the camera can operate at a range of resolutions; from around 2.5 pixels per millimetre on large paintings of up to 2000 × 2000 mm to 10 pixels per millimetre on smaller paintings or details of paintings measuring 500 × 500 mm. The camera is primarily designed to examine underdrawings in paintings; preliminary results from test targets and paintings are presented and the quality of the images compared with those from other cameras currently used in this field

Effect of two behavioural 'nudging' interventions on management decisions for low back pain: A randomised vignette-based study in general practitioners

Objective €Nudges' are subtle cognitive cues thought to influence behaviour. We investigated whether embedding nudges in a general practitioner (GP) clinical decision support display can reduce low-value management decisions. Methods Australian GPs completed four clinical vignettes of patients with low back pain. Participants chose from three guideline-concordant and three guideline-discordant (low-value) management options for each vignette, on a computer screen. A 2×2 factorial design randomised participants to two possible nudge interventions: €partition display' nudge (low-value options presented horizontally, high-value options listed vertically) or €default option' nudge (high-value options presented as the default, low-value options presented only after clicking for more). The primary outcome was the proportion of scenarios where practitioners chose at least one of the low-value care options. Results 120 GPs (72% male, 28% female) completed the trial (n=480 vignettes). Participants using a conventional menu display without nudges chose at least one low-value care option in 42% of scenarios. Participants exposed to the default option nudge were 44% less likely to choose at least one low-value care option (OR 0.56, 95%CI 0.37 to 0.85; p=0.006) compared with those not exposed. The partition display nudge had no effect on choice of low-value care (OR 1.08, 95%CI 0.72 to 1.64; p=0.7). There was no interaction between the nudges (OR 0.94, 95% CI 0.41 to 2.15; p=0.89). Interpretation A default option nudge reduced the odds of choosing low-value options for low back pain in clinical vignettes. Embedding high value options as defaults in clinical decision support tools could improve quality of care. More research is needed into how nudges impact clinical decision-making in different contexts

Data-Driven Segmentation of Post-mortem Iris Images

This paper presents a method for segmenting iris images obtained from the deceased subjects, by training a deep convolutional neural network (DCNN) designed for the purpose of semantic segmentation. Post-mortem iris recognition has recently emerged as an alternative, or additional, method useful in forensic analysis. At the same time it poses many new challenges from the technological standpoint, one of them being the image segmentation stage, which has proven difficult to be reliably executed by conventional iris recognition methods. Our approach is based on the SegNet architecture, fine-tuned with 1,300 manually segmented post-mortem iris images taken from the Warsaw-BioBase-Post-Mortem-Iris v1.0 database. The experiments presented in this paper show that this data-driven solution is able to learn specific deformations present in post-mortem samples, which are missing from alive irises, and offers a considerable improvement over the state-of-the-art, conventional segmentation algorithm (OSIRIS): the Intersection over Union (IoU) metric was improved from 73.6% (for OSIRIS) to 83% (for DCNN-based presented in this paper) averaged over subject-disjoint, multiple splits of the data into train and test subsets. This paper offers the first known to us method of automatic processing of post-mortem iris images. We offer source codes with the trained DCNN that perform end-to-end segmentation of post-mortem iris images, as described in this paper. Also, we offer binary masks corresponding to manual segmentation of samples from Warsaw-BioBase-Post-Mortem-Iris v1.0 database to facilitate development of alternative methods for post-mortem iris segmentation

Training telescope operators and support astronomers at Paranal

The operations model of the Paranal Observatory relies on the work of efficient staff to carry out all the daytime and nighttime tasks. This is highly dependent on adequate training. The Paranal Science Operations department (PSO) has a training group that devises a well-defined and continuously evolving training plan for new staff, in addition to broadening and reinforcing courses for the whole department. This paper presents the training activities for and by PSO, including recent astronomical and quality control training for operators, as well as adaptive optics and interferometry training of all staff. We also present some future plans.Comment: Paper 9910-123 presented at SPIE 201

Using visual analytics to develop situation awareness in astrophysics

We present a novel collaborative visual analytics application for cognitively overloaded users in the astrophysics domain. The system was developed for scientists who need to analyze heterogeneous, complex data under time pressure, and make predictions and time-critical decisions rapidly and correctly under a constant influx of changing data. The Sunfall Data Taking system utilizes several novel visualization and analysis techniques to enable a team of geographically distributed domain specialists to effectively and remotely maneuver a custom-built instrument under challenging operational conditions. Sunfall Data Taking has been in production use for 2 years by a major international astrophysics collaboration (the largest data volume supernova search currently in operation), and has substantially improved the operational efficiency of its users. We describe the system design process by an interdisciplinary team, the system architecture and the results of an informal usability evaluation of the production system by domain experts in the context of Endsley's three levels of situation awareness