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Aerosol particles with rare specific properties act as nuclei for ice formation. The presence of ice nucleating particles in the atmosphere leads to heterogeneous freezing at warm temperatures and thus these particles play an important role in modulating microphysical properties of clouds. This work presents an ice nucleation cold stage instrument for measuring the concentration of ice nucleating particles in liquids. The cost is ∼ $10 k including an external chiller. Using a lower cost heat sink reduces the cost to ∼ $6 k. The instrument is suitable for studying ambient ice nucleating particle concentrations and laboratory-based process-level studies of ice nucleation. The design plans allow individuals to self-manufacture the cold-stage using 3D printing, off-the-shelf parts, and a handful of standard tools. Software to operate the instrument and analyze the data is also provided. The design is intended to be simple enough that a graduate student can build it as part of a course or thesis project. Costs are kept to a minimum to facilitate use in classroom demonstrations and laboratory classes. 

Added support for computing "warm mosaic". The warm mosaic is used to calibrate the temperature lag during ramps. 

Health coaching helps patients identify and accomplish lifestyle-related goals, effectively improving the control of chronic diseases and mitigating mental health conditions. However, health coaching is cost-prohibitive due to its highly personalized and labor-intensive nature. In this paper, we propose to build a dialogue system that converses with the patients, helps them create and accomplish specific goals, and can address their emotions with empathy. However, building such a system is challenging since real-world health coaching datasets are limited and empathy is subtle. Thus, we propose a modularized health coaching dialogue with simplified NLU and NLG frameworks combined with mechanism-conditioned empathetic response generation. Through automatic and human evaluation, we show that our system generates more empathetic, fluent, and coherent responses and outperforms the state-of-the-art in NLU tasks while requiring less annotation. We view our approach as a key step towards building automated and more accessible health coaching systems.