On Astronomical Seeing

1906 ◽  
Vol 25 (1) ◽  
pp. 458-462
Author(s):  
J. Halm

In the Annual Report of the Smithsonian Institution for 1902 Prof. Langley has published an important note on “Good Seeing,” in which he describes some experiments undertaken with the view of improving the definition of telescopic images, so far as it depends on the conditions of the air in the vicinity of the instrument. Up to now the belief has prevailed among astronomers that in order to obtain good definitions the air inside the telescope-tubes should be kept as much as possible not only at a uniform temperature but also in a state of perfect tranquillity. Langley, however, shows that this view is not quite correct, and that maintaining constant and uniform temperature inside the tube, while preventing circulation between the air inside and outside the instrument, is not sufficient to produce satisfactory telescopic images. Particularly, this method does not diminish the troublesome boiling which in solar observations proves so often to be a source of grave inconvenience to the observer. But he shows that if the air inside and near the telescope-tube is agitated by stirring, the definition becomes at once markedly better. The improvement has in all cases been so decided that the reality of this beneficial effect of stirring cannot well be doubted.

2021 ◽  
Author(s):  
Lun Ai ◽  
Stephen H. Muggleton ◽  
Céline Hocquette ◽  
Mark Gromowski ◽  
Ute Schmid

AbstractGiven the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong machine learning (USML). USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance. A recent paper demonstrates the beneficial effect of a machine learned logic theory for a classification task, yet no existing work to our knowledge has examined the potential harmfulness of machine’s involvement for human comprehension during learning. This paper investigates the explanatory effects of a machine learned theory in the context of simple two person games and proposes a framework for identifying the harmfulness of machine explanations based on the Cognitive Science literature. The approach involves a cognitive window consisting of two quantifiable bounds and it is supported by empirical evidence collected from human trials. Our quantitative and qualitative results indicate that human learning aided by a symbolic machine learned theory which satisfies a cognitive window has achieved significantly higher performance than human self learning. Results also demonstrate that human learning aided by a symbolic machine learned theory that fails to satisfy this window leads to significantly worse performance than unaided human learning.


Author(s):  
Paolo Landini

The importance of microorganisms associated with man, the so-called “human microbiota” has become increasingly clear from recent scientific studies. Although it has been known for many years that some microorganisms might have a beneficial effect on processes such as digestion or on the immune system, the specific mechanisms of these phenomena have never been thoroughly studied. However, in recent years the prevalence of either beneficial microorganisms or harmful bacteria, even though not strictly pathogenic, has been associated with pathological conditions such as obesity, Crohn’s disease, atherosclerosis, and other diseases in which a bacterial component had never been implicated. In this report, I describe the main concepts related to the definition of microbiome and the potential impact of studying the mechanisms of man-microbiome interaction on the treatment of several illnesses.


Sign in / Sign up

Export Citation Format

Share Document