This issue is an exploration of the ephemeral—the stray thoughts, the side conversations, the discarded scraps, and false starts that inform a published work of scholarship, usually invisibly. In putting together this collection of work, I wanted to make these traces the focal point, since so much of our actual thinking takes place in spaces of indeterminacy and interpersonal connection. These momentary and provisional collectives— what Anna Lowenhaupt Tsing describes as “patchy assemblages”— spark new modes of working, thinking, and being together in higher education as well as sharing the outputs of such collaboration.1 The pieces in this issue collectively ask the following: What emerges from connection, engagement, amplification, thinkingtogether across disciplinary spaces and ways of knowing? How do we make manifest the relationship- and process-oriented modes of working that shape scholarly thought? And, conversely, how are our thoughts and ideas circumscribed by the institutional, hierarchical, and infrastructural systems we have inherited, as well as the tools and technologies we work with?

In this third article in a series, we investigate the need of spectra denoising for the derivation of stellar parameters. We have used two distinct datasets for this work. The first one contains spectra in the range of 4,450–5,400 Å at a resolution of 42,000, and the second in the range of 8,400–8,800 Å at a resolution of 11,500. We constructed two denoising techniques, an autoencoder, and a principal component analysis. Using random Gaussian noise added to synthetic spectra, we have trained a neural network to derive the stellar parameters Teff , logg, v sini e , ξt, and [M/H] of the denoised spectra. We find that, independently of the denoising technique, the accuracy values of stellar parameters do not improve once we denoise the synthetic spectra. This is true with and without applying data augmentation to the stellar parameters neural network

We report the first description of oculocardiac reflex elicited with injection of local anesthetic in an empty orbit, and highlight clinical indicators for patients that may be at risk for an exaggerated oculocardiac reflex. We describe a patient with prior head and eye trauma treated for anophthalmic socket reconstruction at an outpatient eye surgery center. Injection of local anesthetic into the empty orbit induced an extended sinus arrest. This exaggerated response was avoided in a subsequent surgery by pretreatment with high-dose anticholinergics. (A&A Case Reports. 2017;9:337–8.)

In this third article in a series, we investigate the need of spectra denoising for the derivation of stellar parameters. We have used two distinct datasets for this work. The first one contains spectra in the range of 4,450–5,400 Å at a resolution of 42,000, and the second in the range of 8,400–8,800 Å at a resolution of 11,500. We constructed two denoising techniques, an autoencoder, and a principal component analysis. Using random Gaussian noise added to synthetic spectra, we have trained a neural network to derive the stellar parameters Teff , logg, v sini e , ξt, and [M/H] of the denoised spectra. We find that, independently of the denoising technique, the accuracy values of stellar parameters do not improve once we denoise the synthetic spectra. This is true with and without applying data augmentation to the stellar parameters neural network

Cloud Test

This issue is an exploration of the ephemeral—the stray thoughts, the side conversations, the discarded scraps, and false starts that inform a published work of scholarship, usually invisibly. In putting together this collection of work, I wanted to make these traces the focal point, since so much of our actual thinking takes place in spaces of indeterminacy and interpersonal connection. These momentary and provisional collectives— what Anna Lowenhaupt Tsing describes as “patchy assemblages”— spark new modes of working, thinking, and being together in higher education as well as sharing the outputs of such collaboration.1 The pieces in this issue collectively ask the following: What emerges from connection, engagement, amplification, thinkingtogether across disciplinary spaces and ways of knowing? How do we make manifest the relationship- and process-oriented modes of working that shape scholarly thought? And, conversely, how are our thoughts and ideas circumscribed by the institutional, hierarchical, and infrastructural systems we have inherited, as well as the tools and technologies we work with?