All roads lead to the places of your interest: An on-demand, ride-sharing visitor transport service

Successful visitor transport within large tourist sites should balance visitor experience and operating costs. Inspired by the model of sharing economy, we design a “user-centered” intelligent visitor transport system to improve the efficiency and quality of experience of transport service in large tourist sites. The system’s core approach is a three-stage heuristic model based on Pareto optimality. Results of the proposed service indicate a drastic reduction of visitor delay time and an improvement in energy efficiency. The proposed scheduling schemes for organizers are more diversified and adaptable than the existing service

Enhancement of Transition Temperature in FexSe0.5Te0.5 Film via Iron Vacancies

The effects of iron deficiency in FexSe0.5Te0.5 thin films (0.8<x<1) on superconductivity and electronic properties have been studied. A significant enhancement of the superconducting transition temperature (TC) up to 21K was observed in the most Fe deficient film (x=0.8). Based on the observed and simulated structural variation results, there is a high possibility that Fe vacancies can be formed in the FexSe0.5Te0.5 films. The enhancement of TC shows a strong relationship with the lattice strain effect induced by Fe vacancies. Importantly, the presence of Fe vacancies alters the charge carrier population by introducing electron charge carriers, with the Fe deficient film showing more metallic behavior than the defect-free film. Our study provides a means to enhance the superconductivity and tune the charge carriers via Fe vacancy, with no reliance on chemical doping.Comment: 15 pages, 4 figure

Unsupervised multi-modal style transfer for cardiac MR segmentation

In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire. Our framework mainly consists of two neural networks: a multi-modal image translation network for style transfer and a cascaded segmentation network for image segmentation. The multi-modal image translation network generates realistic and diverse synthetic LGE images conditioned on a single annotated bSSFP image, forming a synthetic LGE training set. This set is then utilized to fine-tune the segmentation network pre-trained on labelled bSSFP images, achieving the goal of unsupervised LGE image segmentation. In particular, the proposed cascaded segmentation network is able to produce accurate segmentation by taking both shape prior and image appearance into account, achieving an average Dice score of 0.92 for the left ventricle, 0.83 for the myocardium, and 0.88 for the right ventricle on the test set

Structured embedding via pairwise relations and long-range interactions in knowledge base

Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. We consider the problem of embedding entities and relations of knowledge bases into low-dimensional continuous vector spaces (distributed representations). Unlike most existing approaches, which are primarily efficient for modelling pairwise relations between entities, we attempt to explicitly model both pairwise relations and long-range interactions between entities, by interpreting them as linear operators on the low-dimensional embeddings of the entities. Therefore, in this paper we introduces path ranking to capture the long-range interactions of knowledge graph and at the same time preserve the pairwise relations of knowledge graph; we call it structured embedding via pairwise relation and /ong-range interactions (referred to as SePLi). Comparing with the-state-of-the-art models, SePLi achieves better performances of embeddings

Investigation of constitutive relationship and dynamic recrystallization behavior of 22MnB5 during hot deformation

In order to analyze the softening behavior of 22MnB5 steel and further predict the constitutive relationship during hot sheet metal forming, a series of isothermal hot compression tests were conducted at the temperature range of 800–950 °C and strain rate range of 0.01–0.8 s−1 on BAEHR 805 A/D thermo-mechanical simulator system. Based on the friction corrected flow curves, the characteristic strain and stress of dynamic recrystallization (DRX) were derived from the Kocks-Mecking plots and expressed as a function of Zener-Hollomon parameter. Moreover, a physical constitutive model considering work hardening (WH), dynamic recovery (DRV) and DRX as well as corresponding JMAK-type DRX kinetics were developed. The results showed that the established physical equations can accurately predict the flow behavior with a correlation coefficient of 0.997 and average absolute relative error of 3.89%. Optical observation of the microstructure after hot compression revealed that the established DRX kinetics accurately reflects the reality, and then a Zener-Hollomon parameter dependent dynamic recrystallized grain size model was developed. Furthermore, EBSD analysis was carried out to study the effect of deformation conditions on martensite morphology and the results show that a lower temperature and higher strain rate lead to a finer martensite packet while the martensite block width becomes larger under the higher strain rate