Scenic: A Language for Scenario Specification and Scene Generation

We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs and sampling these to generate specialized training and test sets. More generally, such languages can be used for cyber-physical systems and robotics to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems like autonomous cars and robots, whose environment is a "scene", a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing "scenarios" that are distributions over scenes. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in a case study on a convolutional neural network designed to detect cars in road images, improving its performance beyond that achieved by state-of-the-art synthetic data generation methods.Comment: 41 pages, 36 figures. Full version of a PLDI 2019 paper (extending UC Berkeley EECS Department Tech Report No. UCB/EECS-2018-8

Segond fracture with anterior cruciate ligament tear in an adolescent

The authors report a case of acute knee injury in a 14-year-old teenager. The X-ray showed a so-called Segond’s fracture: a small avulsed bone fragment, elliptical in shape, lying immediately below the external tibial plateau, a few millimeters from the lateral tibial cortex. The fracture site was in the portion of the tibial condyle which is linked to the middle third of the lateral capsule by meniscal tibial fibers. Clinical examination under anesthesia and subsequent arthroscopy revealed a total intrasubstance ACL (anterior cruciate ligament) tear close to the proximal insertion. The authors confirm Segond’s report of a possible association of this avulsion fracture with ACL injuries, even in adolescence

Mirikizumab as Induction and Maintenance Therapy for Ulcerative Colitis

;irikizumab, a p19-directed antibody against interleukin-23, showed efficacy in the treatment of ulcerative colitis in a phase 2 trial. Methods: We conducted two phase 3, randomized, double-blind, placebo-controlled trials of mirikizumab in adults with moderately to severely active ulcerative colitis. In the induction trial, patients were randomly assigned in a 3:1 ratio to receive mirikizumab (300 mg) or placebo, administered intravenously, every 4 weeks for 12 weeks. In the maintenance trial, patients with a response to mirikizumab induction therapy were randomly assigned in a 2:1 ratio to receive mirikizumab (200 mg) or placebo, administered subcutaneously, every 4 weeks for 40 weeks. The primary end points were clinical remission at week 12 in the induction trial and at week 40 (at 52 weeks overall) in the maintenance trial. Major secondary end points included clinical response, endoscopic remission, and improvement in bowel-movement urgency. Patients who did not have a response in the induction trial were allowed to receive open-label mirikizumab during the first 12 weeks of the maintenance trial as extended induction. Safety was also assessed. Results: A total of 1281 patients underwent randomization in the induction trial, and 544 patients with a response to mirikizumab underwent randomization again in the maintenance trial. Significantly higher percentages of patients in the mirikizumab group than in the placebo group had clinical remission at week 12 of the induction trial (24.2% vs. 13.3%, P<0.001) and at week 40 of the maintenance trial (49.9% vs. 25.1%, P<0.001). The criteria for all the major secondary end points were met in both trials. Adverse events of nasopharyngitis and arthralgia were reported more frequently with mirikizumab than with placebo. Among the 1217 patients treated with mirikizumab during the controlled and uncontrolled periods (including the open-label extension and maintenance periods) in the two trials, 15 had an opportunistic infection (including 6 with herpes zoster infection) and 8 had cancer (including 3 with colorectal cancer). Among the patients who received placebo in the induction trial, 1 had herpes zoster infection and none had cancer. Conclusions: Mirikizumab was more effective than placebo in inducing and maintaining clinical remission in patients with moderately to severely active ulcerative colitis. Opportunistic infection or cancer occurred in a small number of patients treated with mirikizuma

Performing television history

An expanded conception of performance study can disturb current theoretical and historical assumptions about television’s medial identity. The article considers how to write histories of the dominant forms and assumptions about performance in British and American television drama, and analyses how acting is situated in relation to the multiple meaning-making components of television. A longitudinal, wide-ranging analysis is briefly sketched to show that the concept of performance, from acting to the display of television’s mediating capability, can extend to the analysis of how the television medium ‘performed’ its own identity to shape its distinctiveness in specific historical circumstances

Probabilistic Programming

Probabilistic programs are usual functional or imperative programs with two added constructs: (1) the ability to draw values at random from distributions, and (2) the ability to condition values of variables in a program via observations. Models from diverse application areas such as computer vision, coding theory, cryptographic protocols, biology and reliability analysis can be written as probabilistic programs. Probabilistic inference is the problem of computing an explicit representation of the probability distribution implicitly specified by a probabilistic program. Depending on the application, the desired output from inference may vary-we may want to estimate the expected value of some function f with respect to the distribution, or the mode of the distribution, or simply a set of samples drawn from the distribution. In this paper, we describe connections this research area called \Probabilistic Programming&quot; has with programming languages and software engineering, and this includes language design, and the static and dynamic analysis of programs. We survey current state of the art and speculate on promising directions for future research

Impact of vital signs screening & clinician prompting on alcohol and tobacco screening and intervention rates: a pre-post intervention comparison

<p>Abstract</p> <p>Background</p> <p>Though screening and intervention for alcohol and tobacco misuse are effective, primary care screening and intervention rates remain low. Previous studies have increased intervention rates using vital signs screening for tobacco misuse and clinician prompts for screen-positive patients for both alcohol and tobacco misuse. This pilot study's aims were: (1) To determine the feasibility of combined vital signs screening for tobacco and alcohol misuse, (2) To assess the impact of vital signs screening on alcohol and tobacco screening and intervention rates, and (3) To assess the additional impact of tobacco assessment prompts on intervention rates.</p> <p>Methods</p> <p>In five outpatient practices, nurses measuring vital signs were trained to routinely ask a single tobacco question, a prescreening question that identified current drinkers, and the single alcohol screening question for current drinkers. After 4-8 weeks, clinicians were trained in tobacco intervention and nurses were trained to give tobacco abusers a tobacco questionnaire which also served as a clinician intervention prompt. Screening and intervention rates were measured using patient exit interviews (n = 622) at baseline, during the "screening only" period, and during the tobacco prompting phase. Changes in screening and intervention rates were compared using chi square analyses and test of linear trends. Clinic staff were interviewed regarding patient and staff acceptability. Logistic regression was used to evaluate the impact of nurse screening on clinician intervention, the impact of alcohol intervention on concurrent tobacco intervention, and the impact of tobacco intervention on concurrent alcohol intervention.</p> <p>Results</p> <p>Alcohol and tobacco screening rates and alcohol intervention rates increased after implementing vital signs screening (p < .05). During the tobacco prompting phase, clinician intervention rates increased significantly for both alcohol (12.4%, p < .001) and tobacco (47.4%, p = .042). Screening by nurses was associated with clinician advice to reduce alcohol use (OR 13.1; 95% CI 6.2-27.6) and tobacco use (OR 2.6; 95% CI 1.3-5.2). Acceptability was high with nurses and patients.</p> <p>Conclusions</p> <p>Vital signs screening can be incorporated in primary care and increases alcohol screening and intervention rates. Tobacco assessment prompts increase both alcohol and tobacco interventions. These simple interventions show promise for dissemination in primary care settings.</p

Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract