Effect of elevated CO2 and high temperature on seed-set and grain quality of rice

Hybrid vigour may help overcome the negative effects of climate change in rice. A popular rice hybrid (IR75217H), a heat-tolerant check (N22), and a mega-variety (IR64) were tested for tolerance of seed-set and grain quality to high-temperature stress at anthesis at ambient and elevated [CO2]. Under an ambient air temperature of 29 °C (tissue temperature 28.3 °C), elevated [CO2] increased vegetative and reproductive growth, including seed yield in all three genotypes. Seed-set was reduced by high temperature in all three genotypes, with the hybrid and IR64 equally affected and twice as sensitive as the tolerant cultivar N22. No interaction occurred between temperature and [CO2] for seed-set. The hybrid had significantly more anthesed spikelets at all temperatures than IR64 and at 29 °C this resulted in a large yield advantage. At 35 °C (tissue temperature 32.9 °C) the hybrid had a higher seed yield than IR64 due to the higher spikelet number, but at 38 °C (tissue temperature 34–35 °C) there was no yield advantage. Grain gel consistency in the hybrid and IR64 was reduced by high temperatures only at elevated [CO2], while the percentage of broken grains increased from 10% at 29 °C to 35% at 38 °C in the hybrid. It is concluded that seed-set of hybrids is susceptible to short episodes of high temperature during anthesis, but that at intermediate tissue temperatures of 32.9 °C higher spikelet number (yield potential) of the hybrid can compensate to some extent. If the heat tolerance from N22 or other tolerant donors could be transferred into hybrids, yield could be maintained under the higher temperatures predicted with climate change

Deep domain adaptation by weighted entropy minimization for the classification of aerial images

Fully convolutional neural networks (FCN) are successfully used for the automated pixel-wise classification of aerial images and possibly additional data. However, they require many labelled training samples to perform well. One approach addressing this issue is semi-supervised domain adaptation (SSDA). Here, labelled training samples from a source domain and unlabelled samples from a target domain are used jointly to obtain a target domain classifier, without requiring any labelled samples from the target domain. In this paper, a two-step approach for SSDA is proposed. The first step corresponds to a supervised training on the source domain, making use of strong data augmentation to increase the initial performance on the target domain. Secondly, the model is adapted by entropy minimization using a novel weighting strategy. The approach is evaluated on the basis of five domains, corresponding to five cities. Several training variants and adaptation scenarios are tested, indicating that proper data augmentation can already improve the initial target domain performance significantly resulting in an average overall accuracy of 77.5%. The weighted entropy minimization improves the overall accuracy on the target domains in 19 out of 20 scenarios on average by 1.8%. In all experiments a novel FCN architecture is used that yields results comparable to those of the best-performing models on the ISPRS labelling challenge while having an order of magnitude fewer parameters than commonly used FCNs. © 2020 Copernicus GmbH. All rights reserved

Effect of Level Statistics on Superconductivity in Ultrasmall Metallic Grains

We examine the destruction of superconducting pairing in metallic grains as their size is decreased for both even and odd numbers of electrons. This occurs when the average level spacing d is of the same order as the BCS order parameter. The energy levels of these grains are randomly distributed according to random matrix theory, and we must work statistically. We find that the average value of the critical level spacing is larger than for the model of equally spaced levels for both parities, and derive numerically the probabilities $P_{o,e}(d)$ that a grain of mean level spacing d shows pairing.Comment: 12 pages, 2 PostScript files, RevTex format, submitted to PR

Using semantically paired images to improve domain adaptation for the semantic segmentation of aerial images

Modern machine learning, especially deep learning, which is used in a variety of applications, requires a lot of labelled data for model training. Having an insufficient amount of training examples leads to models which do not generalize well to new input instances. This is a particular significant problem for tasks involving aerial images: Often training data is only available for a limited geographical area and a narrow time window, thus leading to models which perform poorly in different regions, at different times of day, or during different seasons. Domain adaptation can mitigate this issue by using labelled source domain training examples and unlabeled target domain images to train a model which performs well on both domains. Modern adversarial domain adaptation approaches use unpaired data. We propose using pairs of semantically similar images, i.e., whose segmentations are accurate predictions of each other, for improved model performance. In this paper we show that, as an upper limit based on ground truth, using semantically paired aerial images during training almost always increases model performance with an average improvement of 4.2% accuracy and .036 mean intersection-over-union (mIoU). Using a practical estimate of semantic similarity, we still achieve improvements in more than half of all cases, with average improvements of 2.5% accuracy and .017 mIoU in those cases. © 2020 Copernicus GmbH. All rights reserved

Dephasing of coupled spin qubit system during gate operations due to background charge fluctuations

It has been proposed that a quantum computer can be constructed based on electron spins in quantum dots or based on a superconducting nanocircuit. During two-qubit operations, the fluctuation of the coupling parameters is a critical factor. One source of such fluctuation is the stirring of the background charges. We focused on the influence of this fluctuation on a coupled spin qubit system. The induced fluctuation in exchange coupling changes the amount of entanglement, fidelity, and purity. In our previous study, the background charge fluctuations were found to be an important channel of dephasing for a single Josephson qubit.Comment: 10 pages, 7 figure. to be publishe

Parity Effect in Ground State Energies of Ultrasmall Superconducting Grains

We study the superconductivity in small grains in the regime when the quantum level spacing $\delta\varepsilon$ is comparable to the gap $\Delta$. As $\delta\varepsilon$ is increased, the system crosses over from superconducting to normal state. This crossover is studied by calculating the dependence of the ground state energy of a grain on the parity of the number of electrons. The states with odd numbers of particles carry an additional energy $\Delta_P$, which shows non-monotonic dependence on $\delta\varepsilon$. Our predictions can be tested experimentally by studying the parity-induced alternation of Coulomb blockade peak spacings in grains of different sizes.Comment: 4 pages, revtex, multicol.st

Deep learning based feature matching and its application in image orientation

Matching images containing large viewpoint and viewing direction changes, resulting in large perspective differences, still is a very challenging problem. Affine shape estimation, orientation assignment and feature description algorithms based on detected hand crafted features have shown to be error prone. In this paper, affine shape estimation, orientation assignment and description of local features is achieved through deep learning. Those three modules are trained based on loss functions optimizing the matching performance of input patch pairs. The trained descriptors are first evaluated on the Brown dataset (Brown et al., 2011), a standard descriptor performance benchmark. The whole pipeline is then tested on images of small blocks acquired with an aerial penta camera, to compute image orientation. The results show that learned features perform significantly better than alternatives based on hand crafted features. © 2020 Copernicus GmbH. All rights reserved