Pea-barley intercrop N dynamics in farmers fields

Knowledge about crop performances in farmers’ fields provides a link between on-farm practice and re-search. Thereby scientists may improve their ability to understand and suggest solutions for the problems facing those who have the responsibility of making sound agricultural decisions. Nitrogen (N) availability is known to be highly heterogeneous in terrestrial plant communities (Stevenson and van Kessel, 1997), a heterogeneity that in natural systems is often associated with variation in the distri-bution of plant species. In intercropping systems the relative proportion of component crops is influenced by the distribution of growth factors such as N in both time and space (Jensen, 1996). In pea-barley intercrops, an increase in the N supply promotes the growth of barley thereby decreasing the N accumulation of pea and giving rise to changes in the relative proportions of the intercropped components (Jensen, 1996). The pres-sure of weeds may, however, significantly change the dynamics in intercrops (Hauggaard-Nielsen et al., 2001). Data from farmers’ fields may provide direct, spatially explicit information for evaluating the poten-tials of improving the utilisation of field variability by intercrops

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

Configuration interaction calculations of the controlled phase gate in double quantum dot qubits

We consider qubit coupling resulting from the capacitive coupling between two double quantum dot (DQD) single-triplet qubits. Calculations of the coupling when the two DQDs are detuned symmetrically or asymmetrically are performed using a full configuration interaction (CI). The full CI reveals behavior that is not observed by more commonly used approximations such as Heitler London or Hund Mulliken, particularly related to the operation of both DQDs in the (0,2) charge sector. We find that there are multiple points in detuning-space where a two-qubit entangling gate can be realized, and that trade-offs between coupling magnitude and sensitivity to fluctuations in detuning make a case for operating the gate in the (0,2) regime not commonly considered.Comment: 4 pages, 5 figure