Surface-sensitive electronic structure of kagome superconductor CsV_(3)Sb_(5)

We systematically study the electronic structure of a kagome superconductor CsV_(3)Sb_(5)at different temperatures coveringboth its charge density wave state and normal state with angle-resolved photoemission spectroscopy.We observe thatthe V-shaped band aroundГshows three different behaviors,referred to as a/a',βandγ,mainly at different temperatures.Detailed investigations confirm that these bands are all from the same bulk Sb-p_(z)origin,but they are quite sensitiveto the sample surface conditions mainly modulated by temperature.Thus,the intriguing temperature dependent electronicbehavior of the band nearГis affected by the sample surface condition,rather than intrinsic electronic behavior originatingfrom the phase transition.Our result systematically reveals the confusing electronic structure behavior of the energy bandsaroundГ,facilitating further exploration of the novel properties in this material.

Attention Based Multi-Patched 3D-CNNs with Hybrid Fusion Architecture for Reducing False Positives during Lung Nodule Detection

In lung nodules there is a huge variation in structural properties like Shape, Surface Texture. Even the spatial properties vary, where they can be found attached to lung walls, blood vessels in complex non-homogenous lung structures. Moreover, the nodules are of small size at their early stage of development. This poses a serious challenge to develop a Computer aided diagnosis (CAD) system with better false positive reduction. Hence, to reduce the false positives per scan and to deal with the challenges mentioned, this paper proposes a set of three diverse 3D Attention based CNN architectures (3D ACNN) whose predictions on given low dose Volumetric Computed Tomography (CT) scans are fused to achieve more effective and reliable results. Attention mechanism is employed to selectively concentrate/weigh more on nodule specific features and less weight age over other irrelevant features. By using this attention based mechanism in CNN unlike traditional methods there was a significant gain in the classification performance. Contextual dependencies are also taken into account by giving three patches of different sizes surrounding the nodule as input to the ACNN architectures. The system is trained and validated using a publicly available LUNA16 dataset in a 10 fold cross validation approach where a competition performance metric (CPM) score of 0.931 is achieved. The experimental results demonstrate that either a single patch or a single architecture in a one-to-one fashion that is adopted in earlier methods cannot achieve a better performance and signifies the necessity of fusing different multi patched architectures. Though the proposed system is mainly designed for pulmonary nodule detection it can be easily extended to classification tasks of any other 3D medical diagnostic computed tomography images where there is a huge variation and uncertainty in classification.