d+id' Chiral Superconductivity in Bilayer Silicene

We investigate the structure and physical properties of the undoped bilayer silicene through first-principles calculations and find the system is intrinsically metallic with sizable pocket Fermi surfaces. When realistic electron-electron interaction turns on, the system is identified as a chiral d+id' topological superconductor mediated by the strong spin fluctuation on the border of the antiferromagnetic spin density wave order. Moreover, the tunable Fermi pocket area via strain makes it possible to adjust the spin density wave critical interaction strength near the real one and enables a high superconducting critical temperature

Managing the noisy glaucomatous test data by self organising maps

One of the main difficulties in obtaining reliable data from patients in glaucomatous tests is the measurement noise caused by the learning effect, inattention, failure of fixation, fatigue, etc. Using Kohonen's self-organising feature maps, we have developed a computational method to distinguish between the noise and true measurement. This method has been shown to provide a satisfactory way of locating and rejecting noise in the test data, an improvement over conventional statistical method

From Node-Line Semimetals to Large Gap QSH States in New Family of Pentagonal Group-IVA Chalcogenide

Two-dimensional (2D) topological insulators (TIs) have attracted tremendous research interest from both theoretical and experimental fields in recent years. However, it is much less investigated in realizing node line (NL) semimetals in 2D materials.Combining first-principles calculations and $k \cdot p$ model, we find that NL phases emerge in p-CS$_2$ and p-SiS$_2$, as well as other pentagonal IVX$_2$ films, i.e. p-IVX$_2$ (IV= C, Si, Ge, Sn, Pb; X=S, Se, Te) in the absence of spin-orbital coupling (SOC). The NLs in p-IVX$_2$ form symbolic Fermi loops centered around the $\Gamma$ point and are protected by mirror reflection symmetry. As the atomic number is downward shifted, the NL semimetals are driven into 2D TIs with the large bulk gap up to 0.715 eV induced by the remarkable SOC effect.The nontrivial bulk gap can be tunable under external biaxial and uniaxial strain. Moreover, we also propose a quantum well by sandwiching p-PbTe$_2$ crystal between two NaI sheets, in which p-PbTe$_2$ still keeps its nontrivial topology with a sizable band gap ($\sim$ 0.5 eV). These findings provide a new 2D materials family for future design and fabrication of NL semimetals and TIs.Comment: 6 pages, 5 figures,2 table

AI for public health: Self-screening for eye diseases

A software-based visual-field testing (perimetry) system is described which incorporates several AI components, including machine learning, an intelligent user interface and pattern discovery. This system has been successfully used for self-screening in several different public environment

Practical Block-wise Neural Network Architecture Generation

Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset.Comment: Accepted to CVPR 201