Spintronics with a Weyl point in superconducting nanostructures

We investigate transport in a superconducting nanostructure housing a Weyl point in the spectrum of Andreev bound states. A minimum magnet state is realized in the vicinity of the point. One or more normal-metal leads are tunnel-coupled to the nanostructure. We have shown that this minimum magnetic setup is suitable for realization of all common goals of spintronics: detection of a magnetic state, conversion of electric currents into spin currents, potentially reaching the absolute limit of one spin per charge transferred, detection of spin accumulation in the leads. The peculiarity and possible advantage of the setup is the ability to switch between magnetic and non-magnetic states by tiny changes of the control parameters: superconducting phase differences. We employ this property to demonstrate the feasibility of less common spintronic effects: spin on demand and alternative spin current.Comment: 9 pages, 6 figure

Note on the Persistence of a Nonautonomous Lotka-Volterra Competitive System with Infinite Delay and Feedback Controls

We study a nonautonomous Lotka-Volterra competitive system with infinite delay and feedback controls. We establish a series of criteria under which a part of n-species of the systems is driven to extinction while the remaining part of the species is persistent. Particularly, as a special case, a series of new sufficient conditions on the persistence for all species of system are obtained. Several examples together with their numerical simulations show the feasibility of our main results

Instance Segmentation of Dense and Overlapping Objects via Layering

Instance segmentation aims to delineate each individual object of interest in an image. State-of-the-art approaches achieve this goal by either partitioning semantic segmentations or refining coarse representations of detected objects. In this work, we propose a novel approach to solve the problem via object layering, i.e. by distributing crowded, even overlapping objects into different layers. By grouping spatially separated objects in the same layer, instances can be effortlessly isolated by extracting connected components in each layer. In comparison to previous methods, our approach is not affected by complex object shapes or object overlaps. With minimal post-processing, our method yields very competitive results on a diverse line of datasets: C. elegans (BBBC), Overlapping Cervical Cells (OCC) and cultured neuroblastoma cells (CCDB). The source code is publicly available

SortedAP: Rethinking evaluation metrics for instance segmentation

Designing metrics for evaluating instance segmentation revolves around comprehensively considering object detection and segmentation accuracy. However, other important properties, such as sensitivity, continuity, and equality, are overlooked in the current study. In this paper, we reveal that most existing metrics have a limited resolution of segmentation quality. They are only conditionally sensitive to the change of masks or false predictions. For certain metrics, the score can change drastically in a narrow range which could provide a misleading indication of the quality gap between results. Therefore, we propose a new metric called sortedAP, which strictly decreases with both object- and pixel-level imperfections and has an uninterrupted penalization scale over the entire domain. We provide the evaluation toolkit and experiment code at https://www.github.com/looooongChen/sortedAP

Driving forces of CO2 emissions and mitigation strategies of China’s National low carbon pilot industrial parks

In an effort to address climate change, in 2013 China launched the world’s largest government-driven carbon emission reduction programme, the National Low Carbon Industrial Parks Pilot Programme (LCIPPP). This paper analyses this newly developed pilot program. To deepen our understanding of the causes and the impact of industrial park CO2 emissions, we use the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and data from 20 pilot industrial parks involved in the LCIPPP for the period 2012–2016. This study quantitatively evaluates the effect of CO2 emissions on output, energy structure, energy intensity, industrial structure, R&D intensity, and population change in different regions and nationally through an elasticity coefficient method. The results confirm that an increase in output and energy intensity is a dominant contributor to the growth of CO2 emissions whereas an increase of the share of tertiary industry and R&D intensity has significant effects on reducing CO2 emissions. The elasticity of energy intensity and renewable energy consumption on CO2 emissions in the eastern region of China is the highest, indicating that using renewable energy to reduce CO2 emissions for the industrial parks is more effective in the eastern region as compared to the central and western regions of the country. The elasticity of population is significantly negative in both the central and western areas while it is positive in eastern part of China, thereby illustrating that promoting labour intensive industries will be an effective way to reduce CO2 emissions for the industrial parks in China’s central and western regions. Our study reveals that differentiated low carbon development pathways should be adopted. Concrete policy implications for reducing CO2 emissions are also provided