PhasePack: A Phase Retrieval Library

Phase retrieval deals with the estimation of complex-valued signals solely from the magnitudes of linear measurements. While there has been a recent explosion in the development of phase retrieval algorithms, the lack of a common interface has made it difficult to compare new methods against the state-of-the-art. The purpose of PhasePack is to create a common software interface for a wide range of phase retrieval algorithms and to provide a common testbed using both synthetic data and empirical imaging datasets. PhasePack is able to benchmark a large number of recent phase retrieval methods against one another to generate comparisons using a range of different performance metrics. The software package handles single method testing as well as multiple method comparisons. The algorithm implementations in PhasePack differ slightly from their original descriptions in the literature in order to achieve faster speed and improved robustness. In particular, PhasePack uses adaptive stepsizes, line-search methods, and fast eigensolvers to speed up and automate convergence

Use of artificial intelligence (AI) in augmentative and alternative communication (AAC):community consultation on risks, benefits and the need for a code of practice

Purpose: This paper reports on a workshop discussing the views of the augmentative and alternative communication (AAC) community on the opportunities and risks posed by the integration of artificial intelligence (AI) into voice output communication aid systems. The views of the community on whether a Code of Practice was needed for the use of this new technology were also sought. Design/methodology/approach: This was an explorative, qualitative study in which members of the AAC community attending a session at a UK national conference were invited to discuss the topic, responding to structured questions from the research team. The use of AI for both novel language generation and rate enhancement was discussed within the session. Findings: Many potential opportunities and benefits of AI to AAC users were discussed by the group. Risks associated with new and existing biases in AI language models were raised, as was the need to ensure that outputs generated by AI were authentically authored by users. Whilst there was broad support for the idea of a Code of Practice, questions were posed about how it would be designed and what it should contain. Originality/value: This study presents a unique insight into the views of the AAC community on the benefits and risks of incorporating AI into AAC systems. The views of the community on the need for a Code of Practice may support how the field moves forward with this complex technology.</p

Learning Symbolic Operators for Task and Motion Planning

Robotic planning problems in hybrid state and action spaces can be solved by integrated task and motion planners (TAMP) that handle the complex interaction between motion-level decisions and task-level plan feasibility. TAMP approaches rely on domain-specific symbolic operators to guide the task-level search, making planning efficient. In this work, we formalize and study the problem of operator learning for TAMP. Central to this study is the view that operators define a lossy abstraction of the transition model of a domain. We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system. Experimentally, we provide results in three domains, including long-horizon robotic planning tasks. We find our approach to substantially outperform several baselines, including three graph neural network-based model-free approaches from the recent literature. Video: https://youtu.be/iVfpX9BpBRo Code: https://git.io/JCT0gComment: IROS 202

GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling

We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards. Inspired by human curiosity, we propose goal-literal babbling (GLIB), a simple and general method for exploration in such problems. GLIB samples relational conjunctive goals that can be understood as specific, targeted effects that the agent would like to achieve in the world, and plans to achieve these goals using the transition model being learned. We provide theoretical guarantees showing that exploration with GLIB will converge almost surely to the ground truth model. Experimentally, we find GLIB to strongly outperform existing methods in both prediction and planning on a range of tasks, encompassing standard PDDL and PPDDL planning benchmarks and a robotic manipulation task implemented in the PyBullet physics simulator. Video: https://youtu.be/F6lmrPT6TOY Code: https://git.io/JIsTBComment: AAAI 202

Overcoming the Pitfalls of Prediction Error in Operator Learning for Bilevel Planning

Bilevel planning, in which a high-level search over an abstraction of an environment is used to guide low-level decision-making, is an effective approach to solving long-horizon tasks in continuous state and action spaces. Recent work has shown how to enable such bilevel planning by learning action and transition model abstractions in the form of symbolic operators and neural samplers. In this work, we show that existing symbolic operator learning approaches fall short in many natural environments where agent actions tend to cause a large number of irrelevant propositions to change. This is primarily because they attempt to learn operators that optimize the prediction error with respect to observed changes in the propositions. To overcome this issue, we propose to learn operators that only model changes necessary for abstract planning to achieve the specified goal. Experimentally, we show that our approach learns operators that lead to efficient planning across 10 different hybrid robotics domains, including 4 from the challenging BEHAVIOR-100 benchmark, with generalization to novel initial states, goals, and objects

Longitudinal patterns of physical activity in children aged 8 to 12 years: The LOOK study

BACKGROUND: Data on longitudinal monitoring of daily physical activity (PA) patterns in youth over successive years is scarce but may provide valuable information for intervention strategies aiming to promote PA. METHODS: Participants were 853 children (starting age ~8 years) recruited from 29 Australian elementary schools. Pedometers were worn for a 7-day period each year over 5 consecutive years to assess PA volume (steps per day) and accelerometers were worn concurrently in the final 2 years to assess PA volume (accelerometer counts (AC) per day), moderate and vigorous PA (MVPA), light PA (LPA) and sedentary time (SED). A general linear mixed model was used to examine daily and yearly patterns. RESULTS: A consistent daily pattern of pedometer step counts, AC, MVPA and LPA emerged during each year, characterised by increases on school days from Monday to Friday followed by a decrease on the weekend. Friday was the most active and Sunday the least active day. The percentage of girls and boys meeting international recommendations of 11,000 and 13,000 steps/day respectively on a Monday, Friday and Sunday were 36%, 50%, 21% for boys and 35%, 45%, 18% for girls. The equivalent percentages meeting the recommended MVPA of >60 min/day on these days were 29%, 39%, 16% for boys and 15%, 21%, 10% for girls. Over the 5 years, boys were more active than girls (mean steps/day of 10,506 vs 8,750; p<0.001) and spent more time in MVPA (mean of 42.8 vs 31.1 min/day; p<0.001). Although there was little evidence of any upward or downward trend in steps/day from age 8 to 12 years, there was a trend toward lower MVPA, LPA and a corresponding increase in SED from age 11 to 12 years. CONCLUSION: A weekly pattern of PA occurred in children as young as age 8 on a day by day basis; these patterns persisting through to age 12. In addition to supporting previous evidence of insufficient PA in children, our data, in identifying the level and incidence of insufficiency on each day of the week, may assist in the development of more specific strategies to increase PA in community based children