Layers of the Atmosphere

In this activity, students learn that the atmosphere can be divided into layers based on temperature changes at different altitudes, by making a graph. They will read the background material, plot data points, and determine where layers begin and end from their comprehension of the reading material. Educational levels: High school, Middle school

Radiative and non radiative muon capture on the proton in heavy baryon chiral perturbation theory

We have evaluated the amplitude for muon capture by a proton, mu + p --> n + nu, to O(p^3) within the context of heavy baryon chiral perturbation theory (HBChPT) using the new O(p^3) Lagrangian of Ecker and Mojzis (E&M). We obtain expressions for the standard muon capture form factors and determine three of the coefficients of the E&M Lagrangian, namely, b_7, b_{19}, and b_{23}. We describe progress on the next step, a calculation of the radiative muon capture process, mu + p --> n + nu + gamma.Comment: Talk at the 15th Int. Conf. on Few-Body Problems in Physics, 22-26 July, 1997, Groningen, The Netherlands, to be published in the proceedings; 5 pages, LaTeX, using espcrc1.st

A simple model illustrating the impossibility of measuring off-shell effects

We consider a simple model and make use of field transformations in Lagrangian field theories to illustrate the impossibility of measuring off-shell effects in nucleon-nucleon bremsstrahlung and related processes.Comment: 3 pages, Latex2e, uses espcrc1.sty, contribution presented at the 16th International Conference on Few-Body Problems in Physics, Taipei, Taiwan, 6-10 March 200

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance. Model-based algorithms, in principle, can provide for much more efficient learning, but have proven difficult to extend to expressive, high-capacity models such as deep neural networks. In this work, we demonstrate that medium-sized neural network models can in fact be combined with model predictive control (MPC) to achieve excellent sample complexity in a model-based reinforcement learning algorithm, producing stable and plausible gaits to accomplish various complex locomotion tasks. We also propose using deep neural network dynamics models to initialize a model-free learner, in order to combine the sample efficiency of model-based approaches with the high task-specific performance of model-free methods. We empirically demonstrate on MuJoCo locomotion tasks that our pure model-based approach trained on just random action data can follow arbitrary trajectories with excellent sample efficiency, and that our hybrid algorithm can accelerate model-free learning on high-speed benchmark tasks, achieving sample efficiency gains of 3-5x on swimmer, cheetah, hopper, and ant agents. Videos can be found at https://sites.google.com/view/mbm

Improving Student Concentration Through Caregiver Education

This action research was conducted in a primary classroom at a Montessori school. Using caregiver education that focused on the importance of limiting screen time, it aimed to increase student concentration during the work cycles. The research collected data through pre and post caregiver attitude scales and questionnaires as well as concentration and observation logs during the morning work cycles. Utilizing both qualitative and quantitative research methods, the data revealed a successful intervention with an increase in student concentration. This study can serve as a framework for future research projects that look at how caregiver education, focusing on different topics, can positively impact a child’s development. This study provided evidence that intentional caregiver education, that both informs and helps build a strong school-family relationship, can support the students’ concentration levels and therefore their development and success in the classroom

Better Briefs

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