A conversation about choreographic thinking tools

This article aims to draw the reader into an interdisciplinary conversation between the co-authors about the use of imagery in dance creation placed under very different disciplinary lenses. The conversation has two points of departure. First, for nearly a decade the choreographer Wayne McGregor has engaged in an interdisciplinary collaborative research with cognitive scientists with the aim to develop new understandings of the choreographic process. A large percentage of this research has focused on imagery in creativity and has resulted in the development of the Choreographic Thinking Tools, currently in use by McGregor and his dance company. One third of this article is dedicated to a description of these developments combined with figures that illustrate the scientific theory lying behind them. The second point of departure and second third of this article brings these ideas into conjunction with somatic practices, as reflected in the writing of an expert practitioner invited to introduce somatics to McGregor\u27s dance company in the framework of the Choreographic Thinking Tools. The final section that concludes the article reintroduces scientific theory with the goal to articulate some of the contrasts and overlaps between the different approaches represented in this conversatio

MILEPOST GCC: machine learning based research compiler

International audienceTuning hardwired compiler optimizations for rapidly evolving hardware makes porting an optimizing compiler for each new platform extremely challenging. Our radical approach is to develop a modular, extensible, self-optimizing compiler that automatically learns the best optimization heuristics based on the behavior of the platform. In this paper we describe MILEPOST GCC, a machine-learning-based compiler that automatically adjusts its optimization heuristics to improve the execution time, code size, or compilation time of specific programs on different architectures. Our preliminary experimental results show that it is possible to considerably reduce execution time of the MiBench benchmark suite on a range of platforms entirely automatically

Benefits for children with suspected cancer from routine whole-genome sequencing

Clinical whole-genome sequencing (WGS) has been shown to deliver potential benefits to children with cancer and to alter treatment in high-risk patient groups. It remains unknown whether offering WGS to every child with suspected cancer can change patient management. We collected WGS variant calls and clinical and diagnostic information from 281 children (282 tumors) across two English units (n = 152 from a hematology center, n = 130 from a solid tumor center) where WGS had become a routine test. Our key finding was that variants uniquely attributable to WGS changed the management in ~7% (20 out of 282) of cases while providing additional disease-relevant findings, beyond standard-of-care molecular tests, in 108 instances for 83 (29%) cases. Furthermore, WGS faithfully reproduced every standard-of-care molecular test (n = 738) and revealed several previously unknown genomic features of childhood tumors. We show that WGS can be delivered as part of routine clinical care to children with suspected cancer and can change clinical management by delivering unexpected genomic insights. Our experience portrays WGS as a clinically impactful assay for routine practice, providing opportunities for assay consolidation and for delivery of molecularly informed patient care.</p

Milepost GCC: Machine Learning Enabled Self-tuning Compiler

International audienceTuning compiler optimizations for rapidly evolving hardwaremakes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization is a popular approach to adapting programs to a new architecture automatically using feedback-directed compilation. However, the large number of evaluations required for each program has prevented iterative compilation from widespread take-up in production compilers. Machine learning has been proposed to tune optimizations across programs systematically but is currently limited to a few transformations, long training phases and critically lacks publicly released, stable tools. Our approach is to develop a modular, extensible, self-tuning optimization infrastructure to automatically learn the best optimizations across multiple programs and architectures based on the correlation between program features, run-time behavior and optimizations. In this paper we describeMilepostGCC, the first publicly-available open-source machine learning-based compiler. It consists of an Interactive Compilation Interface (ICI) and plugins to extract program features and exchange optimization data with the cTuning.org open public repository. It automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture. Part of the MILEPOST technology together with low-level ICI-inspired plugin framework is now included in the mainline GCC.We developed machine learning plugins based on probabilistic and transductive approaches to predict good combinations of optimizations. Our preliminary experimental results show that it is possible to automatically reduce the execution time of individual MiBench programs, some by more than a factor of 2, while also improving compilation time and code size. On average we are able to reduce the execution time of the MiBench benchmark suite by 11% for the ARC reconfigurable processor.We also present a realistic multi-objective optimization scenario for Berkeley DB library using Milepost GCC and improve execution time by approximately 17%, while reducing compilatio

Ondansetron for irritable bowel syndrome with diarrhoea: randomised controlled trial

Background: Irritable bowel syndrome with diarrhoea is characterised by frequent, loose or watery stools with associated urgency, resulting in marked reduction of quality of life. Ondansetron, a 5-hydroxytryptamine-3 receptor antagonist, has been shown to benefit patients with irritable bowel syndrome with diarrhoea.Objective: To evaluate the effect of ondansetron in irritable bowel syndrome with diarrhoea.Design: Phase III, parallel-group, randomised, double-blind, multicentre, placebo-controlled trial in 400 patients, with embedded mechanistic studies.Setting: Hospital, primary care and community.Participants: Eighty participants meeting Rome IV criteria for irritable bowel syndrome with diarrhoea.Intervention: Ondansetron 4 mg (dose titrated up to two tablets three times a day) or matched placebo for 12 weeks.Main outcome measures: Clinical – Primary patient-reported end point was % ‘Food and Drug Administration-defined responders’ over 12 weeks. Secondary end points were worst abdominal pain intensity, worst urgency, stool consistency, stool frequency, anxiety, depression and dyspepsia at 12 and 16 weeks.Main outcome measures: Mechanistic – Whole gut transit time, faecal water, protease (FP), bile acids and assessment of rectal sensitivity using a barostat.Results: Clinical – The study closed early due to slow recruitment. Between 1 January 2018 and 11 May 2020, 80 patients were recruited and randomised (20% of target), 37 to ondansetron, 43 to placebo. Discontinuations (4 ondansetron; 2 placebo) meant 75 completed the 12-week trial treatment. There were four protocol violations. In the intention-to-treat analysis, 15 (40.5%) on ondansetron were primary end-point responders (95% CI 24.7% to 56.4%), and 12 (27.9%) on placebo (95% CI 14.5% to 41.3%), p = 0.19, adjusted OR 1.93 (0.73, 5.11). Pain intensity reduction occurred in 17 (46.0%) on ondansetron (95% CI 29.9% to 62.0%) and 16 (37.2%) on placebo (95% CI 22.8% to 51.7%), p = 0.32. Improvement in stool consistency occurred in 25 (67.6%) on ondansetron (95% CI 52.5% to 82.7%) and 22 (51.2%) on placebo (95% CI 36.2% to 66.1%), p = 0.07. Use of rescue medication, loperamide, was lower on ondansetron [7 (18.9%) vs. 17 (39.5%)]. Average stool consistency in the final month of treatment reduced significantly more on ondansetron, adjusted mean difference –0.5 [standard error (SE) 0.25, 95% CI (–1.0 to –0.02), p = 0.042]. Ondansetron improved dyspepsia score (SFLDQ), adjusted mean difference –3.2 points [SE 1.43, 95% CI (–6.1 to –0.4), p = 0.028]. There were no serious adverse events.Mechanistic – mean (SD). Ondansetron increased whole gut transit time between baseline and week 12 by 3.8 (9.1) hours on ondansetron, significantly more than on placebo –2.2 (10.3), p = 0.01. Mean volume to reach urgency threshold using the barostat increased on ondansetron by 84 (61) ml and 38 (48) ml on placebo, n = 8, p = 0.26. Ondansetron did not significantly alter protease, faecal water or bile acids. Changes in referral pathways substantially reduced referrals, impairing recruitment, which meant the study was underpowered.Conclusion: Our results are consistent with previous studies and confirmed ondansetron improves stool consistency and urgency but showed minor effect on pain. We plan to undertake a simplified version of this trial overcoming the changed referral pathways by recruiting in primary care, using software linked to primary care records to identify and randomise patients with irritable bowel syndrome with diarrhoea to ondansetron or placebo and remotely follow their progress; thus minimising barriers to recruitment.Trial registration: This trial is registered as ISRCTN17508514.Funding: This project was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation programme and will be published in full in Efficacy and Mechanism Evaluation; Vol. 10, No. 9. See the NIHR Journals Library website for further project information