Classifying Imbalanced Data Sets by a Novel RE-Sample and Cost-Sensitive Stacked Generalization Method

Learning with imbalanced data sets is considered as one of the key topics in machine learning community. Stacking ensemble is an efficient algorithm for normal balance data sets. However, stacking ensemble was seldom applied in imbalance data. In this paper, we proposed a novel RE-sample and Cost-Sensitive Stacked Generalization (RECSG) method based on 2-layer learning models. The first step is Level 0 model generalization including data preprocessing and base model training. The second step is Level 1 model generalization involving cost-sensitive classifier and logistic regression algorithm. In the learning phase, preprocessing techniques can be embedded in imbalance data learning methods. In the cost-sensitive algorithm, cost matrix is combined with both data characters and algorithms. In the RECSG method, ensemble algorithm is combined with imbalance data techniques. According to the experiment results obtained with 17 public imbalanced data sets, as indicated by various evaluation metrics (AUC, GeoMean, and AGeoMean), the proposed method showed the better classification performances than other ensemble and single algorithms. The proposed method is especially more efficient when the performance of base classifier is low. All these demonstrated that the proposed method could be applied in the class imbalance problem

Power Amplification and Coherent Combination Techniques for Terahertz Quantum Cascade Lasers

Power amplification and coherent combination are important ways to improve the output power and beam quality of single‐mode terahertz quantum cascade lasers (THz QCLs). Up to date, the tapered waveguide is the most convenient way to amplify the power of THz QCLs. The self‐focusing effect in tapered THz QCLs induces non‐monotonic behaviours of the peak power and far‐field beam divergence, which lead to the existence of optimal structural parameters. The surface and lateral grating techniques have also been employed in tapered THz QCLs to further improve the spectral purity. For coherent combinations, the progress of facet‐emitting phase‐locked arrays of THz QCLs is still limited due to both the lack of the understanding of dynamics of coupled QCLs and the difficulties in designing high‐performance coupled waveguides. We will briefly review the developments of coherent arrays of THz QCLs and present a design of monolithic QCL arrays with common coupled cavity to achieve the optical mutual injection, which may provide a new way for coherent combination of THz QCLs

Optical properties of coupled metal-semiconductor and metal-molecule nanocrystal complexes: the role of multipole effects

We investigate theoretically the effects of interaction between an optical dipole (semiconductor quantum dot or molecule) and metal nanoparticles. The calculated absorption spectra of hybrid structures demonstrate strong effects of interference coming from the exciton-plasmon coupling. In particular, the absorption spectra acquire characteristic asymmetric lineshapes and strong anti-resonances. We present here an exact solution of the problem beyond the dipole approximation and find that the multipole treatment of the interaction is crucial for the understanding of strongly-interacting exciton-plasmon nano-systems. Interestingly, the visibility of the exciton resonance becomes greatly enhanced for small inter-particle distances due to the interference phenomenon, multipole effects, and electromagnetic enhancement. We find that the destructive interference is particularly strong. Using our exact theory, we show that the interference effects can be observed experimentally even in the exciting systems at room temperature.Comment: 9 page

Quantum blockade and loop current induced by a single lattice defect in graphene nanoribbons

We investigate theoretically the electronic transport properties in narrow graphene ribbons with an adatom-induced defect. It is found that the lowest conductance step of a metallic graphene nanoribbon may develop a dip even down to zero at certain values of the Fermi energy due to the defect. Accompanying the occurrence of the conductance dip, a loop current develops around the defect. We show how the properties of the conductance dip depend on the parameters of the defect, such as the relative position and severity of the defect as well as the width and edges of the graphene ribbons. In particular, for metallic armchair-edges graphene nanoribbons, whether the conductance dip appears or not, they can be controlled by choosing the position of the single defect.Comment: 6 pages, 6 figure

Uptake selectivity of methanesulfonic acid (MSA) on fine particles over polynya regions of the Ross Sea, Antarctica

The uptake of methanesulfonic acid (MSA) on existing particles is a major route of the particulate MSA formation, however, MSA uptake on different particles is still lacking in knowledge. Characteristics of MSA uptake on different aerosol particles were investigated in polynya (an area of open sea water surrounded by ice) regions of the Ross Sea, Antarctica. Particulate MSA mass concentrations, as well as aerosol population and size distribution, were observed simultaneously for the first time to access the uptake of MSA on different particles. The results show that MSA mass concentration does not always reflect MSA particle population in the marine atmosphere. MSA uptake on aerosol particle increases the particle size and changes aerosol chemical composition, but it does not increase the particle population. The uptake rate of MSA on particles is significantly influenced by aerosol chemical properties. Sea salt particles are beneficial for MSA uptake, as MSA-Na and MSA-Mg particles are abundant in the Na and Mg particles, accounting for 0.43 +/- 0.21 and 0.41 +/- 0.20 of the total Na and Mg particles, respectively. However, acidic and hydrophobic particles suppress the uptake of MSA, as MSA-EC (elemental carbon) and MSA-SO42- particles account for only 0.24 +/- 0.68 and 0.26 +/- 0.47 of the total EC and SO42- particles, respectively. The results extend the knowledge of the formation and environmental behavior of MSA in the marine atmosphere.Peer reviewe

Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

Accurate location information has significant commercial and economic value as they are widely used in intelligent manufacturing, material localization and smart homes. Magnetic sequence-based approaches show great promise mainly due to their pervasiveness and stability. However, existing geomagnetic indoor localization methods are facing the problems of location ambiguity and feature extraction deficiency, which will lead to large localization errors. To address these issues, we propose a coarse-to-fine geomagnetic indoor localization method based on deep learning. First, a multidimensional geomagnetic feature extraction method is presented which can extract magnetic features from spatial and temporal aspects. Then, a hierarchical deep neural network model is devised to extract more accurate geomagnetic information and corresponding location clues for more accurate localization. Finally, localization is achieved through a particle filter combined with IMU localization. To evaluate the performance of the proposed methods, we carried out several experiments at three trial paths with two heterogeneous devices, Vivo X30 and Huawei Mate30. Experimental results demonstrate that the proposed algorithm can achieve more accurate localization performance than the state-of-the-art methods. Meanwhile, the proposed algorithm has low cost and good pervasiveness for different devices

An improved Multipath Estimating Delay-Lock-Loop method based on Teager-Kaiser operator

In the satellite navigation system, multipath signal propagation effect is the main source of errors to the receiver positioning accuracy. Research has been done on multipath mitigation algorithms but their implementation towards suboptimal or optimal receiver is in demand. One of the key multipath mitigation algorithms is Multipath Estimating DelayLock-Loop (MEDLL), which suffers from large amount of computational complexity, poor real-time performance and error accumulation. To overcome these drawbacks, an improved MEDLL algorithm is proposed here by combining it with the Teager-Kaiser (TK) algorithm and residual signal estimation -- named TK-MEDLL. Simulation results show that the algorithm can accurately estimate the number of multipath signal components, and improve the estimation accuracy of the signal parameters. Besides, TK-MEDLL can separate the multipath from the composite signal, restore the direct signal, and thus effectively suppress the multipath signal. Hence, the time delay estimation accuracy is improved by 0.015chip, whereas the estimation error is reduced by 5%. Overall, the algorithm reduces the computational complexity and improves the real-time performance of signal processing, which is of great significance for satellite communications

Strategies of tuning catalysts for efficient photodegradation of antibiotics in water environments: a review

The photocatalytic degradation of antibiotics is a very promising technique to solve the pollution issues of antibiotics in water. Furthermore, catalysts play a critical role in the photocatalytic process. This article provides the first comprehensive review on the strategies of tuning catalysts for efficient photodegradation of antibiotics. It is shown that the doping of metals and nonmetals, coupling semiconductors, hydrogenation, ligand-to-metal charge transfer effect, and perovskite structure construction are widely exploited to improve visible light activity. Supporting catalysts on mesoporous materials, morphology (size and shape) modification of catalysts, and deposition of metals on the catalysts are demonstrated as efficient approaches for the enhancement of photodegradation efficiency. The generation pathways for reactive oxygen species overi the catalysts, the influencing factors in the photodegradation, and the assessment methods for catalyst performance are evaluated. Finally, the challenges and future research directions are discussed