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Register a new userDoes the period of BE Lyncis really vary?
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New photometric series of BE Lyncis are presented. With template curve fitting we re-determined the $O-C$ for BE Lyncis. The phase shift diagram is apparently constant, disproving the suspected period variations of BE Lyn.
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We discuss how laboratory experiments can be used to place constraints on possible variations of the fine structure constant alpha in the observationally relevant redshift interval z ~= 0 - 5, within a rather general theoretical framework. We find a worst case upper limit for Delta alpha / alpha of 8 x 10^-6 for z <= 5 and Delta alpha / alpha of 0.9 x 10^-6 for z <= 1.6. The derived limits are at variance with the recent findings by Webb et al. (1998), who claim an observed variation of Delta alpha/alpha = -2.6 +- 0.4 x 10^-5 at 1<z<1.6.
SubHalo Abundance Matching (SHAM) assumes that one (sub)halo property, such as mass Mvir or peak circular velocity Vpeak, determines properties of the galaxy hosted in each (sub)halo such as its luminosity or stellar mass. This assumption implies that the dependence of Galaxy Luminosity Functions (GLFs) and the Galaxy Stellar Mass Function (GSMF) on environmental density is determined by the corresponding halo density dependence. In this paper, we test this by determining from an SDSS sample the observed dependence with environmental density of the ugriz GLFs and GSMF for all galaxies, and for central and satellite galaxies separately. We then show that the SHAM predictions are in remarkable agreement with these observations, even when the galaxy population is divided between central and satellite galaxies. However, we show that SHAM fails to reproduce the correct dependence between environmental density and g-r color for all galaxies and central galaxies, although it better reproduces the color dependence on environmental density of satellite galaxies.
Knowledge distillation is a popular technique for training a small student network to emulate a larger teacher model, such as an ensemble of networks. We show that while knowledge distillation can improve student generalization, it does not typically work as it is commonly understood: there often remains a surprisingly large discrepancy between the predictive distributions of the teacher and the student, even in cases when the student has the capacity to perfectly match the teacher. We identify difficulties in optimization as a key reason for why the student is unable to match the teacher. We also show how the details of the dataset used for distillation play a role in how closely the student matches the teacher -- and that more closely matching the teacher paradoxically does not always lead to better student generalization.
The ionization constant of water Kw is currently determined on the proton conductivity sigma1 which is measured at frequencies lower than 10^7 Hz. Here, we develop the idea that the high frequency conductivity sigma2 (~10^11 Hz), rather than sigma1 represents a net proton dynamics in water, to evaluate the actual concentration c of H3O+ and OH- ions from sigma2. We find c to be not dependent on temperature to conclude that i) water electrodynamics is due to a proton exchange between H3O+ (or OH-) ions and neutral H2O molecules rather than spontaneous ionization of H2O molecules, ii) the common Kw (or pH) reflects the thermoactivation of the H3O+ and OH- ions from the potential of their interaction, iii) the lifetime of a target water molecule does not exceed parts of nanosecond.
Moderation is crucial to promoting healthy on-line discussions. Although several `toxicity detection datasets and models have been published, most of them ignore the context of the posts, implicitly assuming that comments maybe judged independently. We investigate this assumption by focusing on two questions: (a) does context affect the human judgement, and (b) does conditioning on context improve performance of toxicity detection systems? We experiment with Wikipedia conversations, limiting the notion of context to the previous post in the thread and the discussion title. We find that context can both amplify or mitigate the perceived toxicity of posts. Moreover, a small but significant subset of manually labeled posts (5% in one of our experiments) end up having the opposite toxicity labels if the annotators are not provided with context. Surprisingly, we also find no evidence that context actually improves the performance of toxicity classifiers, having tried a range of classifiers and mechanisms to make them context aware. This points to the need for larger datasets of comments annotated in context. We make our code and data publicly available.
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