Emergence of a Pseudogap in the BCS-BEC Crossover

Strongly correlated Fermi systems with pairing interactions become superfluid below a critical temperature Tc. The extent to which such pairing correlations alter the behavior of the liquid at temperatures T>Tc is a subtle issue that remains an area of debate, in particular regarding the appearance of the so-called pseudogap in the BCS-BEC crossover of unpolarized spin-1/2 nonrelativistic matter. To shed light on this, we extract several quantities of crucial importance at and around the unitary limit, namely, the odd-even staggering of the total energy, the spin susceptibility, the pairing correlation function, the condensate fraction, and the critical temperature Tc, using a nonperturbative, constrained-ensemble quantum Monte Carlo algorithm

Space-time localization of inner heliospheric plasma turbulence using multiple spacecraft radio links

Radio remote sensing of the heliosphere using spacecraft radio signals has been used to study the near-sun plasma in and out of the ecliptic, close to the sun, and on spatial and temporal scales not accessible with other techniques. Studies of space-time variations in the inner solar wind are particularly timely because of the desire to understand and predict space weather, which can disturb satellites and systems at 1AU and affect human space exploration. Here we demonstrate proof-of-concept of a new radio science application for spacecraft radio science links. The differing transfer functions of plasma irregularities to spacecraft radio up- and downlinks can be exploited to localize plasma scattering along the line of sight. We demonstrate the utility of this idea using Cassini radio data taken in 2001-2002. Under favorable circumstances we demonstrate how this technique, unlike other remote sensing methods, can determine center-of-scattering position to within a few thousandths of an AU and thickness of scattering region to less than about 0.02 AU. This method, applied to large data sets and used in conjunction with other solar remote sensing data such as white light data, has space weather application in studies of inhomogeneity and nonstationarity in the near-sun solar wind.Comment: 28 Pages including 14 Figures (7 unique figures in both inline format and full-page format)

MADNESS: A Multiresolution, Adaptive Numerical Environment for Scientific Simulation

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision based on multiresolution analysis and separated representations. Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability. This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets. BárbaraAvelar-Pereira 9 , EthanRoy2 , Valerie J.Sydnor3,4,5, JasonD.Yeatman1,2, The Fibr Community Science Consortium*, TheodoreD.Satterthwaite3,4,5,88 & Ariel Roke

Development of the Alpha Rhythm Is Linked to Visual White Matter Pathways and Visual Detection Performance

Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase in alpha frequency over childhood and adulthood. Here, we tested the hypothesis that these changes in alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large diffusion MRI (dMRI)-EEG dataset (dMRI n = 2,747, EEG n = 2,561) of children and adolescents of either sex (age range, 5–21 years old) and showed that maturation of the optic radiation specifically accounts for developmental changes of alpha frequency. Behavioral analyses also confirmed that variations of alpha frequency are related to maturational changes in visual perception. The present findings demonstrate the close link between developmental variations in white matter tissue properties, electrophysiological responses, and behavior

Contributed Session I: Specific and non-linear effects of glaucoma on optic radiation tissue properties

Changes in sensory input with aging and disease affect brain tissue properties. To establish the link between glaucoma, the most prevalent cause of irreversible blindness, and changes in major brain connections, we characterized white matter tissue properties in diffusion MRI measurements in a large sample of subjects with glaucoma (N=905; age 49-80) and healthy controls (N=5,292; age 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. A convolutional neural network (CNN) accurately classified whether a subject has glaucoma using information from the primary visual connection to cortex (the optic radiations, OR), but not from non-visual brain connections. On the other hand, regularized linear regression could not classify glaucoma, and the CNN did not generalize to classification of age-group or of age-related macular degeneration. This suggests a unique non-linear signature of glaucoma in OR tissue properties