Plasmonic Tamm states: second enhancement of light inside the plasmonic waveguide

A type of Tamm states inside metal-insulator-metal (MIM) waveguides is proposed. An impedance based transfer matrix method is adopted to study and optimize it. With the participation of the plasmonic Tamm states, fields could be enhanced twice: the ffirst is due to the coupling between a normal waveguide and a nanoscaled plasmonic waveguide and the second is due to the strong localization and field enhancement of Tamm states. As shown in our 2D coupling configuration, |E|^2 is enhanced up to 1050 times when 1550 nm light is coupled from an 300 nm Si slab waveguide into an 40 nm MIM waveguide.Comment: 3 pages, 4 figure

Complementary Labels Learning with Augmented Classes

Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning, which aims to alleviate the annotation cost compared with standard supervised learning. Unfortunately, most previous CLL algorithms were in a stable environment rather than an open and dynamic scenarios, where data collected from unseen augmented classes in the training process might emerge in the testing phase. In this paper, we propose a novel problem setting called Complementary Labels Learning with Augmented Classes (CLLAC), which brings the challenge that classifiers trained by complementary labels should not only be able to classify the instances from observed classes accurately, but also recognize the instance from the Augmented Classes in the testing phase. Specifically, by using unlabeled data, we propose an unbiased estimator of classification risk for CLLAC, which is guaranteed to be provably consistent. Moreover, we provide generalization error bound for proposed method which shows that the optimal parametric convergence rate is achieved for estimation error. Finally, the experimental results on several benchmark datasets verify the effectiveness of the proposed method

Robust unsupervised small area change detection from SAR imagery using deep learning

Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection

Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery

Light-induced dynamics of liquid-crystalline droplets on the surface of iron-doped lithium niobate crystals

We investigated the effect of a photovoltaic field generated on the surface of iron-doped lithium niobate crystals on droplets of a ferroelectric nematic liquid crystalline and a standard nematic liquid crystalline material deposited on this surface. When such assembly is illuminated with a laser beam, a wide range of dynamic phenomena are initiated. Droplets located outside the laser spot are dragged in the direction of the illuminated area, while droplets located inside the illuminated region tend to bridge each other and rearrange into tendril-like structures. In the ferroelectric nematic phase (NF) these processes take place via the formation of conical spikes evolving into jet streams, similar to the behavior of droplets of conventional dielectric liquids exposed to overcritical electric fields. However, in contrast to conventional liquids, the jet streams of the NF phase exhibit profound branching. In the nematic phase (N) of both the ferroelectric nematic and the standard nematic material, dynamic processes occur via smooth-edged continuous features typical for conventional liquids subjected to under-critical fields. The difference in dynamic behavior is attributed to the large increase of dielectric permittivity in the ferroelectric nematic phase with respect to the dielectric permittivity of the nematic phase.Comment: 11 pages, 9 figure

Pushing the resolution limit by correcting the Ewald sphere effect in single-particle Cryo-EM reconstructions

The Ewald sphere effect is generally neglected when using the Central Projection Theorem for cryo electron microscopy single-particle reconstructions. This can reduce the resolution of a reconstruction. Here we estimate the attainable resolution and report a “block-based” reconstruction method for extending the resolution limit. We find the Ewald sphere effect limits the resolution of large objects, especially large viruses. After processing two real datasets of large viruses, we show that our procedure can extend the resolution for both datasets and can accommodate the flexibility associated with large protein complexes

Stirring the Deep, Disentangling the Complexity: Report on the Third Species of Thermochiton (Mollusca: Polyplacophora) From Haima Cold Seeps

This study documents a new deep-sea chiton from the Haima cold seeps. Thermochiton xui. nov. is the third species of the genus Thermochiton and the first occurrence of this genus in the South China Sea. This species is identified by its morphological characteristics and the molecular sequence of a Thermochiton species is reported for the first time. The placement of the new species is determined in the phylogenetic tree of Ischnochitonidae by Maximum Likelihood (ML) and Bayesian inference (BI) methods, based on the sequences of the mitochondrial cytochrome c oxidase subunit I (COI), 16S ribosomal DNA (16S), and nuclear 28S ribosomal DNA (28S) gene regions. Bayesian evolutionary analysis with an uncorrelated relaxed clock approach indicated that this new species is estimated to have diverged from its most closely related shallow-water ischnochitonid taxa 5.10–10.07 million years ago in the Late Miocene. A regional ocean general circulation model was used to estimate the potential dispersal ability of the three species of Thermochiton. Because it is highly unlikely for one species to have spread between the northwest and southwest Pacific to the localities in which this genus has been found to date, we propose that ‘stepping-stone’ habitats and/or ‘bridge species’ were involved in the dispersal and evolution of these cold-seep endemic chitons.The ZooBank Life Science Identifier (LSID) for this publication is: urn:lsid:zoobank.org:pub:AD93E4BC-2977-405E-B681-D956C5C66D83. And the ISID for Thermochiton xui sp. nov. is: urn:lsid:zoobank.org:act:0C75D2E3-F30E-4970-9BC2-3363B397720C