Positron annihilation lifetime spectroscopy of advanced reduced-activation alloy (ARAA) in cold‐worked conditions

Journal of Radioanalytical and Nuclear Chemistry - The defects due to cold-rolling deformation in the advanced reduced-activation alloy (ARAA) have been analyzed using positron annihilation...     

Domain-Specific On-Device Object Detection Method

Object detection is a significant activity in computer vision, and various approaches have been proposed to detect varied objects using deep neural networks (DNNs). However, because DNNs are computation-intensive, it is difficult to apply them to resource-constrained devices. Here, we propose an on-device object detection method using domain-specific models. In the proposed method, we define object of interest (OOI) groups that contain objects with a high frequency of appearance in specific domains. Compared with the existing DNN model, the layers of the domain-specific models are shallower and narrower, reducing the number of trainable parameters; thus, speeding up the object detection. To ensure a lightweight network design, we combine various network structures to obtain the best-performing lightweight detection model. The experimental results reveal that the size of the proposed lightweight model is 21.7 MB, which is 91.35% and 36.98% smaller than those of YOLOv3-SPP and Tiny-YOLO, respectively. The f-measure achieved on the MS COCO 2017 dataset were 18.3%, 11.9% and 20.3% higher than those of YOLOv3-SPP, Tiny-YOLO and YOLO-Nano, respectively. The results demonstrated that the lightweight model achieved higher efficiency and better performance on non-GPU devices, such as mobile devices and embedded boards, than conventional models.     

A scaled version of the double-mean-reverting model for VIX derivatives

As the Heston model is not consistent with VIX data in real market well enough, alternative stochastic volatility models including the double-mean-reverting model of Gatheral (in: Bachelier Congress, 2008) have been developed to overcome its limitation. The double-mean-reverting model is a three factor model successfully reflecting the empirical dynamics of the variance but there is no closed form solution for VIX derivatives and SPX options and thus calibration using conventional techniques may be slow. In this paper, we propose a fast mean-reverting version of the double-mean-reverting model. We obtain a closed form approximation for VIX derivatives and show how it is effective by comparing it with the Heston model and the double-mean-reverting model.     

Study of structural changes for STS 316L using the positron annihilation lifetime spectroscopy

The defects due to extrusion strengths and the temperature dependent deformities for low carbon stainless steel (STS 316L) were investigated using positron annihilation spectroscopy. The defects were restored after tempering at 700 °C. The gradients of the peak on the positron annihilation lifetime spectrum for a sample annealed at 700 °C is increased compared to the as extruded sample. In addition, we calculated the trapping rate and bulk lifetime based on the theoretical model. Both values are decreased, as the extrusion strengths are reduced. All annealed samples showed reduced trapping rate and bulk lifetime, comparing with as extruded samples.