The interface between transition metal compounds provides a rich playground for emergent phenomena. Recently, significantly enhanced superconductivity has been reported for single-layer FeSe on Nb-doped SrTiO 3 substrate. Yet it remains mysterious how the interface affects the superconductivity. Here we use in situ angle-resolved photoemission spectroscopy to investigate various FeSe-based heterostructures grown by molecular beam epitaxy, and uncover that electronic correlations and superconducting gap-closing temperature (T g ) are tuned by interfacial effects. T g up to 75 K is observed in extremely tensile-strained single-layer FeSe on Nb-doped BaTiO 3 , which sets a record high pairing temperature for both Fe-based superconductor and monolayer-thick films, providing a promising prospect on realizing more cost-effective superconducting device. Moreover, our results exclude the direct correlation between superconductivity and tensile strain or the energy of an interfacial phonon mode, and highlight the critical and non-trivial role of FeSe/oxide interface on the high T g , which provides new clues for understanding its origin.

FeSe layer-based superconductors exhibit exotic and distinctive properties. The undoped FeSe shows nematicity and superconductivity, while the heavily electron-doped KxFe2−ySe2 and single-layer FeSe/SrTiO3 possess high superconducting transition temperatures that pose theoretical challenges. However, a comprehensive study on the doping dependence of an FeSe layer-based superconductor is still lacking due to the lack of a clean means of doping control. Through angle-resolved photoemission spectroscopy studies on K-dosed thick FeSe films and FeSe0.93S0.07 bulk crystals, here we reveal the internal connections between these two types of FeSe-based superconductors, and obtain superconductivity below ∼46 K in an FeSe layer under electron doping without interfacial effects. Moreover, we discover an exotic phase diagram of FeSe with electron doping, including a nematic phase, a superconducting dome, a correlation-driven insulating phase and a metallic phase. Such an anomalous phase diagram unveils the remarkable complexity, and highlights the importance of correlations in FeSe layer-based superconductors.

No abstract

Positron emission tomography (PET) imaging is an imaging modality for diagnosing a number of neurological diseases. In contrast to Magnetic Resonance Imaging (MRI), PET is costly and involves injecting a radioactive substance into the patient. Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data. We derive new flow-based generative models which we show perform well in this small sample size regime (much smaller than dataset sizes available in standard vision tasks). Our formulation, DUAL-GLOW, is based on two invertible networks and a relation network that maps the latent spaces to each other. We discuss how given the prior distribution, learning the conditional distribution of PET given the MRI image reduces to obtaining the conditional distribution between the two latent codes w.r.t. the two image types. We also extend our framework to leverage "side" information (or attributes) when available. By controlling the PET generation through "conditioning" on age, our model is also able to capture brain FDG-PET (hypometabolism) changes, as a function of age. We present experiments on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset with 826 subjects, and obtain good performance in PET image synthesis, qualitatively and quantitatively better than recent works.

No abstract