Reinforcement Learning With Simulated User For Automatic Dialog Strategy Optimization

In this paper, we propose a solution to the problem of formulating strategies for a spoken dialog system. Our approach is based on reinforcement learning with the help of a simulated user in order to identify an optimal dialog strategy. Our method considers the Markov decision process to be a framework for representation of speech dialog in which the states represent history and discourse context, the actions are dialog acts and the transition strategies are decisions on actions to take between states. We present our reinforcement learning architecture with a novel objective function that is based on dialog quality rather than its duration

How Consistent Is Competent? Examining Variance in Psychomotor Skills Assessment

Purpose Direct assessment of trainee performance across time is a core tenet of competency-based medical education. Unlike variability of psychomotor skills across levels of expertise, performance variability exhibited by a particular trainee across time remains unexplored. The goal of this study was to document the consistency of individual surgeons\u27 technical skill performance. Method A secondary analysis of assessment data (collected in 2010-2012, originally published in 2015) generated by a prospective cohort of participants at Montreal Children\u27s Hospital with differing levels of expertise was conducted in 2017. Trained raters scored blinded recordings of a myringotomy and tube insertion performed 4 times by junior and senior residents and attending surgeons over a 6-month period using a previously reported assessment tool. Descriptive exploratory analyses and univariate comparison of standard deviations (SDs) were conducted to document variability within individuals across time and across training levels. Results Thirty-six assessments from 9 participants were analyzed. The SD of scores for junior residents was highly variable (5.8 out of a scale of 30 compared with 1.8 for both senior residents and attendings [F(2,19) = 5.68, P \u3c 0.05]). For a given individual, the range of scores was twice as large for junior residents than for senior residents and attendings. Conclusions Surgical residents may display highly variable performances across time, and individual variability appears to decrease with increasing expertise. Operative skill variability could be underrepresented in direct observation assessment; emphasis on an adequate amount of repetitive evaluations for junior residents may be needed to support judgments of competence or entrustment

Search for gravitational waves from Scorpius X-1 with a hidden Markov model in O3 LIGO data

Results are presented for a semicoherent search for continuous gravitational waves from the low-mass x-ray binary Scorpius X-1, using a hidden Markov model (HMM) to allow for spin wandering. This search improves on previous HMM-based searches of Laser Interferometer Gravitational-Wave Observatory data by including the orbital period in the search template grid, and by analyzing data from the latest (third) observing run. In the frequency range searched, from 60 to 500 Hz, we find no evidence of gravitational radiation. This is the most sensitive search for Scorpius X-1 using a HMM to date. For the most sensitive subband, starting at 256.06 Hz, we report an upper limit on gravitational wave strain (at 95% confidence) of h 95 % 0 = 6.16 × 10 − 26 , assuming the orbital inclination angle takes its electromagnetically restricted value ι = 4 4 ° . The upper limits on gravitational wave strain reported here are on average a factor of ∼ 3 lower than in the second observing run HMM search. This is the first Scorpius X-1 HMM search with upper limits that reach below the indirect torque-balance limit for certain subbands, assuming ι = 4 4 °