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Register a new userHistorical Context, Scientific Context, and Translation of Haidingers (1844) Discovery of Naked-Eye Visibility of the Polarization of Light
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Robert P. OShea
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In 1844, the Austrian mineralogist Wilhelm von Haidinger reported he could see the polarization of light with the naked eye. It appears as a faint, blurry, transient, yellow hourglass shape superimposed on whatever one looks at. It is now commonly called Haidingers brushes. To our surprise, even though the paper is well cited, we were unable to find a translation of it from its difficult, nineteenth-century German into English. We provide one, with annotations to set the paper into its scientific and historical context.
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Fine-tuning in physics and cosmology is often used as evidence that a theory is incomplete. For example, the parameters of the standard model of particle physics are unnaturally small (in various technical senses), which has driven much of the search for physics beyond the standard model. Of particular interest is the fine-tuning of the universe for life, which suggests that our universes ability to create physical life forms is improbable and in need of explanation, perhaps by a multiverse. This claim has been challenged on the grounds that the relevant probability measure cannot be justified because it cannot be normalized, and so small probabilities cannot be inferred. We show how fine-tuning can be formulated within the context of Bayesian theory testing (or emph{model selection}) in the physical sciences. The normalizability problem is seen to be a general problem for testing any theory with free parameters, and not a unique problem for fine-tuning. Physical theories in fact avoid such problems in one of two ways. Dimensional parameters are bounded by the Planck scale, avoiding troublesome infinities, and we are not compelled to assume that dimensionless parameters are distributed uniformly, which avoids non-normalizability.
[abridged] WFIRST is uniquely capable of finding planets with masses as small as Mars at separations comparable to Jupiter, i.e., beyond the current ice lines of their stars. These planets fall between the close-in planets found by Kepler and the wide separation gas giants seen by direct imaging and ice giants inferred from ALMA observations. Furthermore, the smallest planets WFIRST can detect are smaller than the planets probed by RV and Gaia at comparable separations. Interpreting planet populations to infer the underlying formation and evolutionary processes requires combining results from multiple detection methods to measure the full variation of planets as a function of planet size, orbital separation, and host star mass. Microlensing is the only way to find planets from 0.5 to 5M_E at 1 to 5au. The case for a microlensing survey from space has not changed in the past 20 yrs: space allows wide-field diffraction-limited observations that resolve main-sequence stars in the bulge, which allows the detection and characterization of the smallest signals including those from planets with masses at least as small as Mars. What has changed is that ground-based (GB) microlensing is reaching its limits, underscoring the scientific necessity for a space-based survey. GB microlensing has found a break in the mass-ratio distribution at about a Neptune, implying that these are the most common microlensing planet and that planets smaller than this are rare. However, GB microlensing reaches its detection limits only slightly below the observed break. WFIRST will measure the shape of the mass-ratio function below the break by finding numerous smaller planets: 500 Neptunes, 600 gas giants, 200 Earths, and planets as small as 0.1M_E. Because it will also measure host masses and distances, WFIRST will also track the behavior of the planet distribution as a function of separation and host star mass.
Recent work in neural machine translation has demonstrated both the necessity and feasibility of using inter-sentential context -- context from sentences other than those currently being translated. However, while many current methods present model architectures that theoretically can use this extra context, it is often not clear how much they do actually utilize it at translation time. In this paper, we introduce a new metric, conditional cross-mutual information, to quantify the usage of context by these models. Using this metric, we measure how much document-level machine translation systems use particular varieties of context. We find that target context is referenced more than source context, and that conditioning on a longer context has a diminishing effect on results. We then introduce a new, simple training method, context-aware word dropout, to increase the usage of context by context-aware models. Experiments show that our method increases context usage and that this reflects on the translation quality according to metrics such as BLEU and COMET, as well as performance on anaphoric pronoun resolution and lexical cohesion contrastive datasets.
This paper examines the predictions made by Chinese, Muslim and Jesuit astronomers of the eclipse of 21 June 1629 in Beijing, allegedly the event that determined Emperor Chongzhens resolution to reform the calendar using the Western method. In order to establish the accuracy of these predictions, as reported at the time by the Chinese scholar and convert Xu Guangqi, we have compared them with an accurate reconstruction of the eclipse made at NASA. In contrast with current opinions, we argue that the prediction made by the Jesuits was indeed the most accurate. It was in fact instrumental in dissipating Chongzhens doubts about the need to entrust Jesuit missionaries serving at the Chinese court with the task of reforming the calendar, leading to the first important scientific collaboration between Europe and China.
Simultaneous machine translation (SiMT) aims to translate a continuous input text stream into another language with the lowest latency and highest quality possible. The translation thus has to start with an incomplete source text, which is read progressively, creating the need for anticipation. In this paper, we seek to understand whether the addition of visual information can compensate for the missing source context. To this end, we analyse the impact of different multimodal approaches and visual features on state-of-the-art SiMT frameworks. Our results show that visual context is helpful and that visually-grounded models based on explicit object region information are much better than commonly used global features, reaching up to 3 BLEU points improvement under low latency scenarios. Our qualitative analysis illustrates cases where only the multimodal systems are able to translate correctly from English into gender-marked languages, as well as deal with differences in word order, such as adjective-noun placement between English and French.
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