Optically reconfigurable metadevices based on phase-change materials

Chalcogenide phase-change media provide a uniquely flexible platform for both nanostructured and optically-rewritable all-dielectric metamaterials. Non-volatile, laser-induced phase transitions enable resonance switching in nanostructured chalcogenide meta-surfaces and allow for reversible direct-writing of arbitrary meta-devices in chalcogenide thin films, including dynamically refocusable, chromatically correctable and super-oscillatory lenses, and near-infrared-resonant photonic metamaterials

Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)

Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in synthesizing the low resolution PET images. To address these limitations, we propose multi-channel generative adversarial networks (M-GAN) based PET image synthesis method. Different to the existing methods which rely on using low-level features, the proposed M-GAN is capable to represent the features in a high-level of semantic based on the adversarial learning concept. In addition, M-GAN enables to take the input from the annotation (label) to synthesize the high uptake regions e.g., tumors and from the computed tomography (CT) images to constrain the appearance consistency and output the synthetic PET images directly. Our results on 50 lung cancer PET-CT studies indicate that our method was much closer to the real PET images when compared with the existing methods.Comment: 9 pages, 2 figure

n-type chalcogenides by ion implantation.

Carrier-type reversal to enable the formation of semiconductor p-n junctions is a prerequisite for many electronic applications. Chalcogenide glasses are p-type semiconductors and their applications have been limited by the extraordinary difficulty in obtaining n-type conductivity. The ability to form chalcogenide glass p-n junctions could improve the performance of phase-change memory and thermoelectric devices and allow the direct electronic control of nonlinear optical devices. Previously, carrier-type reversal has been restricted to the GeCh (Ch=S, Se, Te) family of glasses, with very high Bi or Pb 'doping' concentrations (~5-11 at.%), incorporated during high-temperature glass melting. Here we report the first n-type doping of chalcogenide glasses by ion implantation of Bi into GeTe and GaLaSO amorphous films, demonstrating rectification and photocurrent in a Bi-implanted GaLaSO device. The electrical doping effect of Bi is observed at a 100 times lower concentration than for Bi melt-doped GeCh glasses.This work was supported by the UK EPSRC grants EP/I018417/1, EP/I019065/1 and EP/I018050/1.This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/ncomms634

Stannite quaternary cu2m(M = ni, co)sns4 as low cost inorganic hole transport materials in perovskite solar cells

In this study, inorganic stannite quaternary Cu2M(M = Ni, Co)SnS4 (CMTS) is explored as a low-cost, earth abundant, environmentally friendly and chemically stable hole transport material (HTM). CMTS nanoparticles were synthesized via a facile and mild solvothermal method and processed into aggregated nanoparticle inks, which were applied in n-i-p perovskite solar cells (PSCs). The results show that Cu2NiSnS4 (CNiTS) is more promising as an HTM than Cu2CoSnS4 (CCoTS), showing efficient charge injection as evidenced by considerable photoluminescence quenching and lower series resistance from Nyquist plots, as well as higher power conversion efficiency (PCE). Moreover, the perovskite layer coated by the CMTS HTM showed superior environmental stability after 200 h light soaking in 50% relative humidity, while organic HTMs suffer from a severe drop in perovskite absorption. Although the obtained PCEs are modest, this study shows that the cost effective and stable inorganic CMTSs are promising HTMs, which can contribute towards PSC commercialization, if the field can further optimize CMTS energy levels through compositional engineering.</jats:p

Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images

Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for anomaly screening. For this ultrasound (US) is employed. While expert sonographers are adept at reading US images, MR images are much easier for non-experts to interpret. Hence in this paper we seek to produce images with MRI-like appearance directly from clinical US images. Our own clinical motivation is to seek a way to communicate US findings to patients or clinical professionals unfamiliar with US, but in medical image analysis such a capability is potentially useful, for instance, for US-MRI registration or fusion. Our model is self-supervised and end-to-end trainable. Specifically, based on an assumption that the US and MRI data share a similar anatomical latent space, we first utilise an extractor to determine shared latent features, which are then used for data synthesis. Since paired data was unavailable for our study (and rare in practice), we propose to enforce the distributions to be similar instead of employing pixel-wise constraints, by adversarial learning in both the image domain and latent space. Furthermore, we propose an adversarial structural constraint to regularise the anatomical structures between the two modalities during the synthesis. A cross-modal attention scheme is proposed to leverage non-local spatial correlations. The feasibility of the approach to produce realistic looking MR images is demonstrated quantitatively and with a qualitative evaluation compared to real fetal MR images.Comment: MICCAI-MLMI 201

Feature Selection via Chaotic Antlion Optimization

Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used.This work was partially supported by the IPROCOM Marie Curie initial training network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grants agreement No. 316555, and by the Romanian National Authority for Scientific Research, CNDIUEFISCDI, project number PN-II-PT-PCCA-2011-3.2- 0917. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript