Nonlinear Meissner effect in a high-temperature superconductor: Local versus nonlocal electrodynamics

Measured intermodulation distortion (IMD) power at 1.5 GHz in a series of YBa[subscript 2]Y[subscript 3]O[subscript 7−δ] stripline resonators of varying strip widths is compared to the predictions of two qualitatively distinct theories of the nonlinear Meissner effect. The stripline resonators are patterned from a single wafer to ensure uniformity of the material properties. According to the first theory [T. Dahm and D. J. Scalapino, Phys. Rev. B 60, 13125 (1999)], the IMD power is dominated by contributions from the strip edges, while according to the second theory [D. Agassi and D. E. Oates, Phys. Rev. B 72, 014538 (2005)] it is dominated by contributions from the body of the strip. The parameter-free comparison of the measured data with the theoretical predictions clearly favors the latter theory. We conclude that the nonlinear component of the penetration depth must be treated with nonlocal electrodynamics. The origins of this outcome are discussed briefly in the framework of a Green’s-function approach

Lineage dynamics of murine pancreatic development at single-cell resolution.

Organogenesis requires the complex interactions of multiple cell lineages that coordinate their expansion, differentiation, and maturation over time. Here, we profile the cell types within the epithelial and mesenchymal compartments of the murine pancreas across developmental time using a combination of single-cell RNA sequencing, immunofluorescence, in situ hybridization, and genetic lineage tracing. We identify previously underappreciated cellular heterogeneity of the developing mesenchyme and reconstruct potential lineage relationships among the pancreatic mesothelium and mesenchymal cell types. Within the epithelium, we find a previously undescribed endocrine progenitor population, as well as an analogous population in both human fetal tissue and human embryonic stem cells differentiating toward a pancreatic beta cell fate. Further, we identify candidate transcriptional regulators along the differentiation trajectory of this population toward the alpha or beta cell lineages. This work establishes a roadmap of pancreatic development and demonstrates the broad utility of this approach for understanding lineage dynamics in developing organs

Recent Star Formation in Sextans A

We investigate the relationship between the spatial distributions of stellar populations and of neutral and ionized gas in the Local Group dwarf irregular galaxy Sextans A. This galaxy is currently experiencing a burst of localized star formation, the trigger of which is unknown. We have resolved various populations of stars via deep UBV(RI)_C imaging over an area with diameter \sim 5.'3. We have compared our photometry with theoretical isochrones appropriate for Sextans A, in order to determine the ages of these populations. We have mapped out the history of star formation, most accurately for times \lesssim 100 Myr. We find that star formation in Sextans A is correlated both in time and space, especially for the most recent (\lesssim 12 Myr) times. The youngest stars in the galaxy are forming primarily along the inner edge of the large H I shell. Somewhat older populations, \lesssim 50 Myr, are found inward of the youngest stars. Progressively older star formation, from \sim 50--100 Myr, appears to have some spatially coherent structure and is more centrally concentrated. The oldest stars we can accurately sample appear to have approximately a uniform spatial distribution, which extends beyond a surface brightness of \mu_B \simeq 25.9 mag arcsec^{-2} (or, a radius r \simeq 2.'3$). Although other processes are also possible, our data provides support for a mechanism of supernova-driven expansion of the neutral gas, resulting in cold gas pileup and compression along the H I shell and sequential star formation in recent times.Comment: 64 pages, 22 figures, to appear in A

Brain-informed speech separation (BISS) for enhancement of target speaker in multitalker speech perception

Hearing-impaired people often struggle to follow the speech stream of an individual talker in noisy environments. Recent studies show that the brain tracks attended speech and that the attended talker can be decoded from neural data on a single-trial level. This raises the possibility of “neuro-steered” hearing devices in which the brain-decoded intention of a hearing-impaired listener is used to enhance the voice of the attended speaker from a speech separation front-end. So far, methods that use this paradigm have focused on optimizing the brain decoding and the acoustic speech separation independently. In this work, we propose a novel framework called brain-informed speech separation (BISS)1 in which the information about the attended speech, as decoded from the subject’s brain, is directly used to perform speech separation in the front-end. We present a deep learning model that uses neural data to extract the clean audio signal that a listener is attending to from a multi-talker speech mixture. We show that the framework can be applied successfully to the decoded output from either invasive intracranial electroencephalography (iEEG) or non-invasive electroencephalography (EEG) recordings from hearing-impaired subjects. It also results in improved speech separation, even in scenes with background noise. The generalization capability of the system renders it a perfect candidate for neuro-steered hearing-assistive devices

Mean Field Dynamics in Non-Abelian Plasmas from Classical Transport Theory

Based on classical transport theory, we present a general set of covariant equations describing the dynamics of mean fields and their statistical fluctuations in a non-Abelian plasma in or out-of-equilibrium. A procedure to obtain the collision integrals for the Boltzmann equation from the microscopic theory is described. As an application, we study a hot non-Abelian plasma close to equilibrium, where the fluctuations are integrated out explicitly. For soft fields, and at logarithmic accuracy, we obtain B\"odeker's effective theory.Comment: 4 pages, revtex, no figures. Typo removed, a reference updated, version as to appear in Phys. Rev. Let

A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on linear mappings between features of sound streams and EEG responses (forward model), or vice versa (backward model). It has been shown that when the envelope of attended speech and EEG responses are used to derive such mapping functions, the model estimates can be used to discriminate between attended and unattended talkers. However, the predictive/reconstructive performance of the models is dependent on how the model parameters are estimated. There exist a number of model estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different estimation methods to classify attended speakers from multi-channel EEG data. The performance of the model estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams. We find that when forward models predict the EEG from the attended audio, regularized models do not improve regression or classification accuracies. When backward models decode the attended speech from the EEG, regularization provides higher regression and classification accuracies

Determination of the Bending Rigidity of Graphene via Electrostatic Actuation of Buckled Membranes

The small mass and atomic-scale thickness of graphene membranes make them highly suitable for nanoelectromechanical devices such as e.g. mass sensors, high frequency resonators or memory elements. Although only atomically thick, many of the mechanical properties of graphene membranes can be described by classical continuum mechanics. An important parameter for predicting the performance and linearity of graphene nanoelectromechanical devices as well as for describing ripple formation and other properties such as electron scattering mechanisms, is the bending rigidity, {\kappa}. In spite of the importance of this parameter it has so far only been estimated indirectly for monolayer graphene from the phonon spectrum of graphite, estimated from AFM measurements or predicted from ab initio calculations or bond-order potential models. Here, we employ a new approach to the experimental determination of {\kappa} by exploiting the snap-through instability in pre-buckled graphene membranes. We demonstrate the reproducible fabrication of convex buckled graphene membranes by controlling the thermal stress during the fabrication procedure and show the abrupt switching from convex to concave geometry that occurs when electrostatic pressure is applied via an underlying gate electrode. The bending rigidity of bilayer graphene membranes under ambient conditions was determined to be $35.5^{+20}_{-15}$ eV. Monolayers have significantly lower {\kappa} than bilayers

Understanding Urban Demand for Wild Meat in Vietnam: Implications for Conservation Actions

Vietnam is a significant consumer of wildlife, particularly wild meat, in urban restaurant settings. To meet this demand, poaching of wildlife is widespread, threatening regional and international biodiversity. Previous interventions to tackle illegal and potentially unsustainable consumption of wild meat in Vietnam have generally focused on limiting supply. While critical, they have been impeded by a lack of resources, the presence of increasingly organised criminal networks and corruption. Attention is, therefore, turning to the consumer, but a paucity of research investigating consumer demand for wild meat will impede the creation of effective consumer-centred interventions. Here we used a mixed-methods research approach comprising a hypothetical choice modelling survey and qualitative interviews to explore the drivers of wild meat consumption and consumer preferences among residents of Ho Chi Minh City, Vietnam. Our findings indicate that demand for wild meat is heterogeneous and highly context specific. Wild-sourced, rare, and expensive wild meat-types are eaten by those situated towards the top of the societal hierarchy to convey wealth and status and are commonly consumed in lucrative business contexts. Cheaper, legal and farmed substitutes for wild-sourced meats are also consumed, but typically in more casual consumption or social drinking settings. We explore the implications of our results for current conservation interventions in Vietnam that attempt to tackle illegal and potentially unsustainable trade in and consumption of wild meat and detail how our research informs future consumer-centric conservation actions

A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on linear mappings between features of sound streams and EEG responses (forward model), or vice versa (backward model). It has been shown that when the envelope of attended speech and EEG responses are used to derive such mapping functions, the model estimates can be used to discriminate between attended and unattended talkers. However, the predictive/reconstructive performance of the models is dependent on how the model parameters are estimated. There exist a number of model estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different estimation methods to classify attended speakers from multi-channel EEG data. The performance of the model estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams. We find that when forward models predict the EEG from the attended audio, regularized models do not improve regression or classification accuracies. When backward models decode the attended speech from the EEG, regularization provides higher regression and classification accuracies

Observational Constraints on the Molecular Gas Content in Nearby Starburst Dwarf Galaxies

Using star formation histories derived from optically resolved stellar populations in nineteen nearby starburst dwarf galaxies observed with the Hubble Space Telescope, we measure the stellar mass surface densities of stars newly formed in the bursts. By assuming a star formation efficiency (SFE), we then calculate the inferred gas surface densities present at the onset of the starbursts. Assuming a SFE of 1%, as is often assumed in normal star-forming galaxies, and assuming that the gas was purely atomic, translates to very high HI surface densities (~10^2-10^3 Msun pc^-2), which are much higher than have been observed in dwarf galaxies. This implies either higher values of SFE in these dwarf starburst galaxies or the presence of significant amounts of H_2 in dwarfs (or both). Raising the assumed SFEs to 10% or greater (in line with observations of more massive starbursts associated with merging galaxies), still results in HI surface densities higher than observed in 10 galaxies. Thus, these observations appear to require that a significant fraction of the gas in these dwarf starbursts galaxies was in the molecular form at the onset of the bursts. Our results imply molecular gas column densities in the range 10^19-10^21 cm^-2 for the sample. In those galaxies where CO observations have been made, these densities correspond to values of the CO-H_2 conversion factor (X_CO) in the range >3-80x10^20 cm^-2 (K km s^-1)^-1, or up to 40x greater than Galactic X_CO values.Comment: 8 pages, 4 figures, 2 table