基于Delaunay三角法的电压门控钾离子通道的分类和拓扑结构识别

本文构建了一个Delaunay模型对电压门控钾离子(Kv)通道进行预测。该方法通过建立一系列带有隐含变量的单纯形来分类电压门控钾离子通道,然后通过未知序列在主成分空间里面的投影来预测结构。结果表明该模型无论是在分类和结构预测上均可以得到很好的结果。     

Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas

This paper studies the privacy of wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect transmissions of interest. There exists one transmitter that transmits to its receiver in the presence of an eavesdropper. In the meantime, a cooperative jammer (CJ) with multiple antennas transmits carefully crafted adversarial perturbations over the air to fool the eavesdropper into classifying the received superposition of signals as noise. While generating the adversarial perturbation at the CJ, multiple antennas are utilized to improve the attack performance in terms of fooling the eavesdropper. Two main points are considered while exploiting the multiple antennas at the adversary, namely the power allocation among antennas and the utilization of channel diversity. To limit the impact on the bit error rate (BER) at the receiver, the CJ puts an upper bound on the strength of the perturbation signal. Performance results show that this adversarial perturbation causes the eavesdropper to misclassify the received signals as noise with a high probability while increasing the BER at the legitimate receiver only slightly. Furthermore, the adversarial perturbation is shown to become more effective when multiple antennas are utilized.     

Incorporating crossed classification credibility into the Lee–Carter model for multi-population mortality data

Recent developments in actuarial literature have shown that credibility theory can serve as an effective tool in mortality modelling, leading to accurate forecasts when applied to single or multi-population datasets. This paper presents a crossed classification credibility formulation of the Lee–Carter method particularly designed for multi-population mortality modelling. Differently from the standard Lee–Carter methodology, where the time index is assumed to follow an appropriate time series process, herein, future mortality dynamics are estimated under a crossed classification credibility framework, which models the interactions between various risk factors (e.g. genders, countries). The forecasting performances between the proposed model, the original Lee–Carter model and two multi-population Lee–Carter extensions are compared for both genders of multiple countries. Numerical results indicate that the proposed model produces more accurate forecasts than the Lee–Carter type models, as evaluated by the mean absolute percentage forecast error measure. Applications with life insurance and annuity products are also provided and a stochastic version of the proposed model is presented.     

Solving a classification task by spiking neural network with STDP based on rate and temporal input encoding

This paper develops local learning algorithms to solve a classification task with the help of biologically inspired mathematical models of spiking neural networks involving the mechanism of spike-timing-dependent plasticity (STDP). The advantages of the models are their simplicity and, hence, the potential ability to be hardware-implemented in low-energy-consuming biomorphic computing devices. The methods developed are based on two key effects observed in neurons with STDP: mean firing rate stabilization and memorizing repeating spike patterns. As the result, two algorithms to solve a classification task with a spiking neural network are proposed: the first based on rate encoding of the input data and the second based on temporal encoding. The accuracy of the algorithms is tested on the benchmark classification tasks of Fisher's Iris and Wisconsin breast cancer, with several combinations of input data normalization and preprocessing. The respective accuracies are 99% and 94% by F1-score.     

合成孔径雷达极化成像解译识别技术的进展与展望

极化合成孔径雷达(SAR)能够获取目标的全极化信息，在对地观测、灾害评估、侦察监视等民用和军用领域得到广泛应用。国内主要高校、中科院、工业部门和用户单位在该领域开展了卓有成效的工作，取得一大批标志性研究成果。该文简要综述了极化SAR成像解译识别领域的主要研究进展。在解译层面，主要介绍了极化目标分解和极化旋转域解译等理论方法的研究进展。在应用层面，结合研究团队的工作，探讨了上述理论方法在舰船检测、地物分类和建筑物损毁评估等领域的应用成效。最后，对极化SAR目标解译识别技术的研究进行了展望。      

调制方式的动态TOPSIS识别

提出了基于动态理想解法（TOPSIS）的调制识别方法，将各特征参数看成一个整体，对各个调制方式判别打分，根据得分高低来判定调制样式。该方法简单、方便，弥补了传统判决树法判决门限难于选取的缺点，同时克服了神经网络法训练时间长、对算法要求高的难题。仿真验证了所提方法的有效性，在信噪比10 dB以上时正确识别率接近100%。     

基于一位全加器三变量逻辑函数的查表综合

通过对1个一位全加器3个输入端不同的组合，可以实现与门、或门、非门、同或门及异或门，因此全加器在数字逻辑电路中有着重要的作用．本文在介绍全加器的基础上提出了用查表法设计基于一位全加器实现任意三变量函数的组合电路和时序电路．在与传统的与非门／或非门的比较中，它显示了优势．     

Parallel application of block-iterative methods in medical imaging and radiation therapy

Some row-action algorithms which exploit special objective function and constraints structure have proven advantageous for solving huge and sparse feasibility or optimization problems. Recently developed block-iterative versions of such special-purpose methods enable parallel computation when the underlying problem is appropriately decomposed. This opens the door for parallel computation in image reconstruction problems of computerized tomography and in the inverse problem of radiation therapy treatment planning, all in their fully discretized modelling approach. Since there is more than one way of deriving block-iterative versions of any row-action method, the choice has to be made with reference to the underlying real-world problem.This research was done with partial support of National Institutes of Health, Grant HL-28438 while the author was with the Medical Image Processing Group (MIPG) at the Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.