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Abstract We investigate the properties and relationship between Doppler velocity fluctuations and intensity fluctuations in the off-limb quiet Sun corona. These are expected to reflect the properties of Alfvénic and compressive waves, respectively. The data come from the Coronal Multichannel Polarimeter (COMP). These data were studied using spectral methods to estimate the power spectra, amplitudes, perpendicular correlation lengths, phases, trajectories, dispersion relations, and propagation speeds of both types of fluctuations. We find that most velocity fluctuations are due to Alfvénic waves but that intensity fluctuations come from a variety of sources, likely including fast and slow mode waves, as well as aperiodic variations. The relation between the velocity and intensity fluctuations differs depending on the underlying coronal structure. On short closed loops, the velocity and intensity fluctuations have similar power spectra and speeds. In contrast, on longer nearly radial trajectories, the velocity and intensity fluctuations have different power spectra, with the velocity fluctuations propagating at much faster speeds than the intensity fluctuations. Considering the temperature sensitivity of COMP, these longer structures are more likely to be closed fields lines of the quiet Sun rather than cooler open field lines. That is, we find the character of the interactions of Alfvénic waves and density fluctuations depends on the length of the magnetic loop on which they are traveling. 

Abstract We have studied the propagation of inertial Alfvén waves through parallel gradients in the Alfvén speed using the Large Plasma Device at the University of California, Los Angeles. The reflection and transmission of Alfvén waves through inhomogeneities in the background plasma are important for understanding wave propagation, turbulence, and heating in space, laboratory, and astrophysical plasmas. Here we present inertial Alfvén waves under conditions relevant to solar flares and the solar corona. We find that the transmission of the inertial Alfvén waves is reduced as the sharpness of the gradient is increased. Any reflected waves were below the detection limit of our experiment, and reflection cannot account for all of the energy not transmitted through the gradient. Our findings indicate that, for both kinetic and inertial Alfvén waves, the controlling parameter for the transmission of the waves through an Alfvén speed gradient is the ratio of the Alfvén wavelength along the gradient divided by the scale length of the gradient. Furthermore, our results suggest that an as-yet-unidentified damping process occurs in the gradient. 

Abstract We compare a method for inferring the photospheric vector magnetic field using only spectroscopy to a conventional method based on polarimetry. The magnetic field strengthBand inclination angle can be inferred from the Zeeman splitting using only StokesI. We applied this method to a sunspot observed with the Vacuum Tower Telescope and compared the results to vector magnetograms from the Helioseismic and Magnetic Imager on the Solar Dynamics Observatory, which used a polarimetric inversion. The spectroscopic inversion tends to show higher values inBcompared to the polarimetric data. In quiet regions the discrepancy inBwas typically a factor of two. In the strong sunspot fields, the differences averaged ≈22%. These discrepancies are significant, but comparable to those typically found among magnetograms from different instruments. Our results support the use of the spectroscopic inversion technique to provide a fast and reasonable estimate ofB. 

A major goal of psycholinguistic theory is to account for the cognitive constraints limiting the speed and ease of language comprehension and production. Wide-ranging evidence demonstrates a key role for linguistic expectations: A word’s predictability, as measured by the information-theoretic quantity of surprisal, is a major determinant of processing difficulty. But surprisal, under standard theories, fails to predict the difficulty profile of an important class of linguistic patterns: the nested hierarchical structures made possible by recursion in human language. These nested structures are better accounted for by psycholinguistic theories of constrained working memory capacity. However, progress on theory unifying expectation-based and memory-based accounts has been limited. Here we present a unified theory of a rational trade-off between precision of memory representations with ease of prediction, a scaled-up computational implementation using contemporary machine learning methods, and experimental evidence in support of the theory’s distinctive predictions. We show that the theory makes nuanced and distinctive predictions for difficulty patterns in nested recursive structures predicted by neither expectation-based nor memory-based theories alone. These predictions are confirmed 1) in two language comprehension experiments in English, and 2) in sentence completions in English, Spanish, and German. More generally, our framework offers computationally explicit theory and methods for understanding how memory constraints and prediction interact in human language comprehension and production. 

Abstract We have obtained constraints on the nanoflare energy distribution and timing for the heating of a coronal bright point. Observations of the bright point were made using the Extreme Ultraviolet Imaging Spectrometer on Hinode in slot mode, which collects a time series of monochromatic images of the region leading to unambiguous temperature diagnostics. The Enthalpy-Based Thermal Evolution of Loops model was used to simulate nanoflare heating of the bright point and generate a time series of synthetic intensities. The nanoflare heating in the model was parameterized in terms of the power-law index α of the nanoflare energy distribution, which is ∝ E − α ; average nanoflare frequency f ; and the number N of magnetic strands making up the observed loop. By comparing the synthetic and observed light curves, we inferred the region of the model parameter space ( α , f , N ) that was consistent with the observations. Broadly, we found that N and f are inversely correlated with one another, while α is directly correlated with either N or f . These correlations are likely a consequence of the region requiring a certain fixed energy input, which can be achieved in various ways by trading off among the different parameters. We also find that a value of α > 2 generally gives the best match between the model and observations, which indicates that the heating is dominated by low-energy events. Our method of using monochromatic images, focusing on a relatively simple structure, and constraining nanoflare parameters on the basis of statistical properties of the intensity provides a versatile approach to better understand the nature of nanoflares and coronal heating. 

Abstract While natural languages differ widely in both canonical word order and word order flexibility, their word orders still follow shared cross-linguistic statistical patterns, often attributed to functional pressures. In the effort to identify these pressures, prior work has compared real and counterfactual word orders. Yet one functional pressure has been overlooked in such investigations: The uniform information density (UID) hypothesis, which holds that information should be spread evenly throughout an utterance. Here, we ask whether a pressure for UID may have influenced word order patterns cross-linguistically. To this end, we use computational models to test whether real orders lead to greater information uniformity than counterfactual orders. In our empirical study of 10 typologically diverse languages, we find that: (i) among SVO languages, real word orders consistently have greater uniformity than reverse word orders, and (ii) only linguistically implausible counterfactual orders consistently exceed the uniformity of real orders. These findings are compatible with a pressure for information uniformity in the development and usage of natural languages.1 

Abstract We find evidence for the first observation of the parametric decay instability (PDI) in the lower solar atmosphere. In particular, we find that the power spectrum of density fluctuations near the solar transition region resembles the power spectrum of the velocity fluctuations but with the frequency axis scaled up by about a factor of 2. These results are from an analysis of the Si iv lines observed by the Interface Region Imaging Spectrometer in the transition region of a polar coronal hole. We also find that the density fluctuations have radial velocity of about 75 km s −1 and that the velocity fluctuations are much faster with an estimated speed of 250 km s −1 , as is expected for sound waves and Alfvén waves, respectively, in the transition region. Theoretical calculations show that this frequency relationship is consistent with those expected from PDI for the plasma conditions of the observed region. These measurements suggest an interaction between sound waves and Alfvén waves in the transition region, which is evidence for the parametric decay instability. 

Linguistic typology generally divides synthetic languages into groups based on their morphological fusion. However, this measure has long been thought to be best considered a matter of degree. We present an information-theoretic measure, called informational fusion, to quantify the degree of fusion of a given set of morphological features in a surface form, which naturally provides such a graded scale. Informational fusion is able to encapsulate not only concatenative, but also nonconcatenative morphological systems (e.g. Arabic), abstracting away from any notions of morpheme segmentation. We then show, on a sample of twenty-one languages, that our measure recapitulates the usual linguistic classifications for concatenative systems, and provides new measures for nonconcatenative ones. We also evaluate the long-standing hypotheses that more frequent forms are more fusional, and that paradigm size anticorrelates with degree of fusion. We do not find evidence for the idea that languages have characteristic levels of fusion; rather, the degree of fusion varies across part-of-speech within languages. 

Abstract We introduce a theoretical framework for understanding and predicting the complexity of sequence classification tasks, using a novel extension of the theory of Boolean function sensitivity. The sensitivity of a function, given a distribution over input sequences, quantifies the number of disjoint subsets of the input sequence that can each be individually changed to change the output. We argue that standard sequence classification methods are biased towards learning low-sensitivity functions, so that tasks requiring high sensitivity are more difficult. To that end, we show analytically that simple lexical classifiers can only express functions of bounded sensitivity, and we show empirically that low-sensitivity functions are easier to learn for LSTMs. We then estimate sensitivity on 15 NLP tasks, finding that sensitivity is higher on challenging tasks collected in GLUE than on simple text classification tasks, and that sensitivity predicts the performance both of simple lexical classifiers and of vanilla BiLSTMs without pretrained contextualized embeddings. Within a task, sensitivity predicts which inputs are hard for such simple models. Our results suggest that the success of massively pretrained contextual representations stems in part because they provide representations from which information can be extracted by low-sensitivity decoders. 

A woody dicot (hybrid poplar), an herbaceous dicot (goldenrod), and a graminaceous monocot (corn stover) were subjected to alkaline hydrogen peroxide (AHP) pretreatment and subsequent enzymatic hydrolysis in order to assess how taxonomically and structurally diverse biomass feedstocks respond to a mild alkaline oxidative pretreatment and how differing features of the cell wall matrix contribute to its recalcitrance. Using glycome profiling, we determined changes in the extractability of non-cellulosic glucans following pretreatment by screening extracts of the pretreated walls with a panel of 155 cell wall glycan-directed monoclonal antibodies to determine differences in the abundance and distribution of non-cellulosic glycan epitopes in these extracts and assess pretreatment-induced changes in the structural integrity of the cell wall. Two taxonomically-dependent outcomes of pretreatment were identified that both improved the subsequent enzymatic hydrolysis yields but differed in their impacts on cell wall structural integrity. Specifically, it was revealed that goldenrod walls exhibited decreases in all classes of alkali-extractable glycans indicating their solubilization during pretreatment, which was accompanied by an improvement in the subsequent extractability of the remaining cell wall glycans. The corn stover walls did not show the same decreases in glycan abundance in extracts following pretreatment, but rather mild increases in all classes of cell wall glycans, indicating overall weaker associations between cell wall polymers and improved extractability. The hybrid poplar walls were relatively unaffected by pretreatment in terms of composition, enzymatic hydrolysis, and the extractability of cell wall glycans due presumably to their higher lignin content and denser vascular structure.