Friendly spectrum jamming against MIMO eavesdropping

Wireless Networks - Friendly spectrum jamming is a flexible scheme to establish secure communications among heterogeneous wireless devices without the need of encryption. Previous works have...     

High Q 2D-length extension mode resonators for potential time–frequency applications

Microsystem Technologies - This paper presents recent advances on two dimensional length-extension mode (2D-LEM) quartz resonators providing high quality (Q) factor on resonances at a few MHz. The...     

Reliable before-fabrication forecasting of MEMS piezoresistive pressure sensor: mathematical modelling and numerical simulation

Microsystem Technologies - Micro-mechanical systems (MEMS) based piezoresistive pressure sensors have significant importance in several pressure sensor devices in real world, i.e., aviation, IoT...     

Hierarchical classification of data streams: a systematic literature review

The classification task usually works with flat and batch learners, assuming problems as stationary and without relations between class labels. Nevertheless, several real-world problems do not assume these premises, i.e., data have labels organized hierarchically and are made available in streaming fashion, meaning that their behavior can drift over time. Existing studies on hierarchical classification do not consider data streams as input of their process, and thus, data is assumed as stationary and handled through batch learners. The same can be said about works on streaming data, as the hierarchical classification is overlooked. Studies concerning each area individually are promising, yet, do not tackle their intersection. This study analyzes the main characteristics of the state-of-the-art works on hierarchical classification for streaming data concerning five aspects: (i) problems tackled, (ii) datasets, (iii) algorithms, (iv) evaluation metrics, and (v) research gaps in the area. We performed a systematic literature review of primary studies and retrieved 3,722 papers, of which 42 were identified as relevant and used to answer the aforementioned research questions. We found that the problems handled by hierarchical classification of data streams include mainly classification of images, human activities, texts, and audio; the datasets are mostly created or synthetic data; the algorithms and evaluation metrics are well-known techniques or based on those; and research gaps are related to dynamic context, data complexity, and computational resources constraints. We also provide implications for future research and experiments to consider common characteristics shared amongst hierarchical classification and data stream classification.

     

Cross-Domain Gated Learning for Domain Generalization

Domain generalization aims to improve the generalization capacity of a model by leveraging useful information from the multi-domain data. However, learning an effective feature representation from such multi-domain data is challenging, due to the domain shift problem. In this paper, we propose an information gating strategy, termed cross-domain gating (CDG), to address this problem. Specifically, we try to distill the domain-invariant feature by adaptively muting the domain-related activations in the feature maps. This feature distillation process prevents the network from overfitting to the domain-related detailed information, and thereby improves the generalization ability of learned feature representation. Extensive experiments are conducted on three public datasets. The experimental results show that the proposed CDG training strategy can excellently enforce the network to exploit the intrinsic features of objects from the multi-domain data, and achieve a new state-of-the-art domain generalization performance on these benchmarks.

     

A heterogeneous multi-attribute case retrieval method based on neutrosophic sets and TODIM for emergency situations

Applied Intelligence - Heterogeneous multi-attribute case retrieval is a crucial step in generating emergency alternatives during the course of emergency decision making (EDM) by referring to...     

基于因果干预与不变性的卷积预训练模型优化研究

基于卷积神经网络（CNN）的深度模型在图像识别与分类领域应用广泛,但在全局特征控制、概念层次特征不变性提取和变量之间的因果关系确定方面仍存在不足,使得深度模型缺乏灵活性、适应性及泛化性。基于因果干预和不变性,提出一种基于CNN深度模型的定向修剪和网络结构优化方法。通过对模型输入进行基于不变性的干预调制,根据生成的调制图片序列分析预训练网络卷积子结构的输出分布,筛选和定向修剪噪声敏感子结构。构建基于类间区分度的目标函数,借助经济学领域中的资本资产定价模型构建网络的层间连接,生成在单分类任务下能增大类间区分度的网络拓扑结构,逐层优化构建概念层次的稳定特征。在ImageNet-2012数据集上的实验结果表明,优化后的深度模型相比于ResNet50基线预训练模型的分类准确率约提升了5个百分点,并大幅降低了训练集规模。     

Tungsten wires and porous NiAl prepared through directional solidification and selective dissolution

The combination of directional solidification and selective dissolution was applied to fabricate tungsten (W) wires and porous NiAl matrix. A NiAl–W pseudobinary eutectic alloy with 1.5?at.% tungsten was directionally solidified in a Bridgman-type oven at 1700°C. Results confirmed that the relationships of the growth rate with the interfibrous spacing and diameter of W fibrous phases in the directionally solidified samples are in accordance with the Jackson and Hunt (J?H) model. Afterward, the NiAl matrix was selectively dissolved in an HCl:H₂O₂ solution to reveal W wires, which present various three-dimensional (3D) morphologies at different growth rates. The W fibrous phases in the NiAl–W alloy samples were then selectively removed with a mixed etchant of ammonium acetate to form a porous NiAl matrix at a constant potential. Dynamic corrosion curves revealed that etching W from the NiAl matrix was inhibited after 2–3?h. The porous structures of NiAl after removing W phases are linked to the 3D morphologies of W fibrous phases embedded in the NiAl matrix. The aspect ratio of W wires and the structures of porous NiAl can be adjusted by selecting the process parameters of this combined technology.     

Pathogen-mimicking nanocomplexes: self-stimulating oxidative stress in tumor microenvironment for chemo-immunotherapy

A novel drug-loaded pathogen-mimicking nanocomplex (MSN-DOX-SP-LPS) has been constructed for synergistic chemo-immunotherapy.

     

Regulation of cell fate by near infrared-responsive telomerase activity

Here we develop a strategy using near infrared (NIR) modulation of telomerase activity based on gold nanocage@smart polymer system. Using this biocompatible design, we can regulate cellular behavior. This system has been used in vivo by taking advantages of NIR. This is the first example for optical modulation of telomerase activity in living cells and tissues.