Tunneling magnetoresistance in diluted magnetic semiconductor tunnel junctions

Using the spin-polarized tunneling model and taking into account the basic physics of ferromagnetic semiconductors, we study the temperature dependence of the tunneling magnetoresistance (TMR) in the diluted magnetic semiconductor (DMS) trilayer heterostructure system (Ga,Mn)As/AlAs/(Ga,Mn)As. The experimentally observed TMR ratio is in reasonable agreement with our result based on the typical material parameters. It is also shown that the TMR ratio has a strong dependence on both the itinerant-carrier density and the magnetic ion density in the DMS electrodes. This can provide a potential way to achieve larger TMR ratio by optimally adjusting the material parameters.Comment: 5 pages (RevTex), 3 figures (eps), submitted to PR

Prediction and analysis of the residual capacity of concrete-filled steel tube stub columns under axial compression subjected to combined freeze-thaw cycles and acid rain corrosion

© 2019 by the authors. This paper presents a theoretical investigation on the safety evaluation, stability evaluation, and service life prediction of concrete-filled steel tube (CFST) structures in a Northern China area with acid rain. The finite element software ABAQUS was used to establish a numerical model, which was used to simulate the axial compression behavior of CFST columns subjected to the combined actions of freeze-thaw cycles and acid rain corrosion. The model performance was validated using the experimental results of the evaluation of mechanical properties, including the failure mode and load-displacement curve. Then, the effects of the section size, material strength, steel ratio, and combined times on the residual capacity were studied. The results show that the section size has a smaller influence on the residual strength than the other parameters and can be neglected in the design procedure. However, the other parameters, including the material strength, steel ratio, and combined times have relatively large influences on the axial compressive performance of CFST stub columns subjected to a combination of freeze-thaw cycles and acid rain corrosion. Finally, design formulae for predicting the residual strength of CFST stub columns that are under axial compression and the combined effect of freeze-thaw cycles and acid rain corrosion are proposed, and their results agree well with the numerical results

A Novel Local Community Detection Method Using Evolutionary Computation.

The local community detection is a significant branch of the community detection problems. It aims at finding the local community to which a given starting node belongs. The local community detection plays an important role in analyzing the complex networks and recently has drawn much attention from the researchers. In the past few years, several local community detection algorithms have been proposed. However, the previous methods only make use of the limited local information of networks but overlook the other valuable information. In this article, we propose an evolutionary computation-based algorithm called evolutionary-based local community detection (ELCD) algorithm to detect local communities in the complex networks by taking advantages of the entire obtained information. The performance of the proposed algorithm is evaluated on both synthetic and real-world benchmark networks. The experimental results show that the proposed algorithm has a superior performance compared with the state-of-the-art local community detection methods. Furthermore, we test the proposed algorithm on incomplete real-world networks to show its effectiveness on the networks whose global information cannot be obtained

Detecting time-fragmented cache attacks against AES using Performance Monitoring Counters

Cache timing attacks use shared caches in multi-core processors as side channels to extract information from victim processes. These attacks are particularly dangerous in cloud infrastructures, in which the deployed countermeasures cause collateral effects in terms of performance loss and increase in energy consumption. We propose to monitor the victim process using an independent monitoring (detector) process, that continuously measures selected Performance Monitoring Counters (PMC) to detect the presence of an attack. Ad-hoc countermeasures can be applied only when such a risky situation arises. In our case, the victim process is the AES encryption algorithm and the attack is performed by means of random encryption requests. We demonstrate that PMCs are a feasible tool to detect the attack and that sampling PMCs at high frequencies is worse than sampling at lower frequencies in terms of detection capabilities, particularly when the attack is fragmented in time to try to be hidden from detection

Bidirectional multi-scale attention networks for semantic segmentation of oblique UAV imagery

Semantic segmentation for aerial platforms has been one of the fundamental scene understanding task for the earth observation. Most of the semantic segmentation research focused on scenes captured in nadir view, in which objects have relatively smaller scale variation compared with scenes captured in oblique view. The huge scale variation of objects in oblique images limits the performance of deep neural networks (DNN) that process images in a single scale fashion. In order to tackle the scale variation issue, in this paper, we propose the novel bidirectional multi-scale attention networks, which fuse features from multiple scales bidirectionally for more adaptive and effective feature extraction. The experiments are conducted on the UAVid2020 dataset and have shown the effectiveness of our method. Our model achieved the state-of-the-art (SOTA) result with a mean intersection over union (mIoU) score of 70.80%

Marginalized average attentional network for weakly-supervised learning

© 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved. In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions. To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the most salient regions in a principled manner. The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion. MAA samples multiple subsets from the video snippet features according to a set of latent discriminative probabilities and takes the expectation over all the averaged subset features. Theoretically, we prove that the MAA module with learned latent discriminative probabilities successfully reduces the difference in responses between the most salient regions and the others. Therefore, MAAN is able to generate better class activation sequences and identify dense and integral action regions in the videos. Moreover, we propose a fast algorithm to reduce the complexity of constructing MAA from O(2T) to O(T2). Extensive experiments on two large-scale video datasets show that our MAAN achieves a superior performance on weakly-supervised temporal action localization

Investigating users’ perspectives on the development of bike-sharing in Shanghai

High levels of car dependence have caused tremendous challenges for sustainable transport development. Transport planners, therefore, seek ways of replacing motor vehicles, as well as increasing the proportion of active travel. The bike-sharing scheme can be seen as an effective way of doing so, particularly in Asian cities. The aim of this paper is to investigate users’ perspectives on the development of bike-sharing using Shanghai as an example. Semi-structured interviews are used to examine the main factors motivating and impeding the development of the bike-sharing scheme in Shanghai. Our findings show that convenience, saving time and financial savings are the major motivations; whereas problems with bicycles being poorly maintained and abused by users, operational issues, financial issues and an unsuitable business model are the major obstacles. In addition, the findings also suggest that a public and private partnership could be the best option for running a sustainable bike-sharing scheme with clear areas of responsibility. Financial incentives, a bicycle-friendly infrastructure, regular operational management and supportive policies should be prioritised. In order to achieve the targets set by the Shanghai Master Plan 2035, transport planners and policymakers should integrate the bike-sharing scheme within the wider active travel system

Chitosan based fibrous absorbents for indoxyl sulfate sorption

Standard dialyzer membranes, designed for diffusive clearance, do not effectively clear protein-bound uremic toxins, such as indoxyl sulfate (IS). To increase protein-bound toxins removal, absorbents require a high specific surface area to achieve effective size-coupling removal of target toxins. However, the toxicity of a molecule is not necessarily determined by size alone. As proof of concept, we report on an electrospun polycaprolactone/chitosan (PCL/CS) fibrous absorbent for IS removal based on chemical structural interaction. A single unit (20 mm in length) of our PCL/CS absorbent achieved a 28% clearance of IS within an hour at both 40 mg/L and 5 mg/L concentrations in a single pass model. This fibrous absorbent structure offers new thoughts on absorbent design