Twin-free YBa2Cu3O7 films on (001) NdGaO3 showing isotropic electrical behaviour

Investigating the epitaxial nature of YBa2Cu3O7 films on NdGaO3 (001) by Rutherford backscattering (RBS) and X-ray diffraction (XRD) texture measurements we find that the films are almost single crystalline, in the sense that the a, b and c axes are uniquely defined with respect to those of NdGaO3. The crystalline perfection is, however, not reflected in the electrical properties of the films. Although we measure a Tc of 89.7 K, we did not observe the expected anisotropy in the resistivity. We interpret this to be due to Ga diffusion from the substrate into the film, which effectively blocks the chain conductivity

Conformality Lost

We consider zero-temperature transitions from conformal to non-conformal phases in quantum theories. We argue that there are three generic mechanisms for the loss of conformality in any number of dimensions: (i) fixed point goes to zero coupling, (ii) fixed point runs off to infinite coupling, or (iii) an IR fixed point annihilates with a UV fixed point and they both disappear into the complex plane. We give both relativistic and non-relativistic examples of the last case in various dimensions and show that the critical behavior of the mass gap behaves similarly to the correlation length in the finite temperature Berezinskii-Kosterlitz-Thouless (BKT) phase transition in two dimensions, xi ~ exp(c/|T-T_c|^{1/2}). We speculate that the chiral phase transition in QCD at large number of fermion flavors belongs to this universality class, and attempt to identify the UV fixed point that annihilates with the Banks-Zaks fixed point at the lower end of the conformal window.Comment: 30 pages, 6 figures; v2: typos fixed, references adde

Uncertainty in the determination of soil hydraulic parameters and its influence on the performance of two hydrological models of different complexity

Data of soil hydraulic properties forms often a limiting factor in unsaturated zone modelling, especially at the larger scales. Investigations for the hydraulic characterization of soils are time-consuming and costly, and the accuracy of the results obtained by the different methodologies is still debated. However, we may wonder how the uncertainty in soil hydraulic parameters relates to the uncertainty of the selected modelling approach. We performed an intensive monitoring study during the cropping season of a 10 ha maize field in Northern Italy. The data were used to: i) compare different methods for determining soil hydraulic parameters and ii) evaluate the effect of the uncertainty in these parameters on different variables (i.e. evapotranspiration, average water content in the root zone, flux at the bottom boundary of the root zone) simulated by two hydrological models of different complexity: SWAP, a widely used model of soil moisture dynamics in unsaturated soils based on Richards equation, and ALHyMUS, a conceptual model of the same dynamics based on a reservoir cascade scheme. We employed five direct and indirect methods to determine soil hydraulic parameters for each horizon of the experimental profile. Two methods were based on a parameter optimization of: a) laboratory measured retention and hydraulic conductivity data and b) field measured retention and hydraulic conductivity data. The remaining three methods were based on the application of widely used Pedo-Transfer Functions: c) Rawls and Brakensiek, d) HYPRES, and e) ROSETTA. Simulations were performed using meteorological, irrigation and crop data measured at the experimental site during the period June – October 2006. Results showed a wide range of soil hydraulic parameter values generated with the different methods, especially for the saturated hydraulic conductivity Ksat and the shape parameter a of the van Genuchten curve. This is reflected in a variability of the modeling results which is, as expected, different for each model and each variable analysed. The variability of the simulated water content in the root zone and of the bottom flux for different soil hydraulic parameter sets is found to be often larger than the difference between modeling results of the two models using the same soil hydraulic parameter set. Also we found that a good agreement in simulated soil moisture patterns may occur even if evapotranspiration and percolation fluxes are significantly different. Therefore multiple output variables should be considered to test the performances of methods and model

Eigenlevel statistics of the quantum adiabatic algorithm

We study the eigenlevel spectrum of quantum adiabatic algorithm for 3-satisfiability problem, focusing on single-solution instances. The properties of the ground state and the associated gap, crucial for determining the running time of the algorithm, are found to be far from the predictions of random matrix theory. The distribution of gaps between the ground and the first excited state shows an abundance of small gaps. Eigenstates from the central part of the spectrum are, on the other hand, well described by random matrix theory.Comment: 8 pages, 10 ps figure

Palynostratigraphy and palaeoenvironments of the Rævekløft, Gule Horn and Ostreaelv Formations (Lower–Middle Jurassic), Neill Klinter Group, Jameson Land, East Greenland

The Neill Klinter Group of Jameson Land, East Greenland contains rich and diverse palynomorph assemblages. Spores, pollen and freshwater algae dominate most of the samples, but dinoflagellate cysts and acritarchs also form important components. The ages suggested by the palynomorphs from the Rævekløft, Gule Horn and Ostreaelv Formations span the period from the Early Pliensbachian to the early Aalenian. The number of palynomorphs identified totals 136, including 83 miospore and 53 microplankton species; they are grouped into seven palynological assemblage zones. In general, there is good agreement between the palynological and sedimentological data, and the palynological data has refined the understanding of the depositional palaeoenvironments of the Neill Klinter Group. In some cases, the boundaries of the palynological assemblage zones are congruent with major sequence stratigraphic surfaces and the palynological data thus support the sequence stratigraphic interpretation. In other cases, however, regional correlation indicates that the zone boundaries cross important sequence stratigraphic surfaces, such as sequence boundaries; such behaviour is thought to reflect the facies-dependent nature of certain of the palynological assemblage zones. The pattern of palynological events in East Greenland has also been recognised on the mid-Norwegian shelf

Tensor Networks for Medical Image Classification

With the increasing adoption of machine learning tools like neural networks across several domains, interesting connections and comparisons to concepts from other domains are coming to light. In this work, we focus on the class of Tensor Networks, which has been a work horse for physicists in the last two decades to analyse quantum many-body systems. Building on the recent interest in tensor networks for machine learning, we extend the Matrix Product State tensor networks (which can be interpreted as linear classifiers operating in exponentially high dimensional spaces) to be useful in medical image analysis tasks. We focus on classification problems as a first step where we motivate the use of tensor networks and propose adaptions for 2D images using classical image domain concepts such as local orderlessness of images. With the proposed locally orderless tensor network model (LoTeNet), we show that tensor networks are capable of attaining performance that is comparable to state-of-the-art deep learning methods. We evaluate the model on two publicly available medical imaging datasets and show performance improvements with fewer model hyperparameters and lesser computational resources compared to relevant baseline methods.Comment: Accepted for publication at International Conference on Medical Imaging with Deep Learning (MIDL), 2020. Reviews on Openreview here: https://openreview.net/forum?id=jjk6bxk07

Lung Segmentation from Chest X-rays using Variational Data Imputation

Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible, making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the high opacity regions as missing data and present a modified CNN-based image segmentation network that utilizes a deep generative model for data imputation. We train this model on normal CXRs with extensive data augmentation and demonstrate the usefulness of this model to extend to cases with extreme abnormalities.Comment: Accepted to be presented at the first Workshop on the Art of Learning with Missing Values (Artemiss) hosted by the 37th International Conference on Machine Learning (ICML). Source code, training data and the trained models are available here: https://github.com/raghavian/lungVAE

Scaling of running time of quantum adiabatic algorithm for propositional satisfiability

We numerically study quantum adiabatic algorithm for the propositional satisfiability. A new class of previously unknown hard instances is identified among random problems. We numerically find that the running time for such instances grows exponentially with their size. Worst case complexity of quantum adiabatic algorithm therefore seems to be exponential.Comment: 7 page