FSPAM:A Feature Construction Method to Identifying Cell Populations in ScRNA-seq Data

The emergence of single-cell RNA-sequencing(scRNA-seq)technology has introduced new information about the structure of cells,diseases,and their associated biological factors.One of the main uses of scRNA-seq is identifying cell populations,which sometimes leads to the detection of rare cell populations.However,the new method is still in its infancy and with its advantages comes computational challenges that are just beginning to address.An important tool in the analysis is dimensionality reduction,which transforms high dimensional data into a meaningful reduced subspace.The technique allows noise removal,visualization and compression of high-dimensional data.This paper presents a new dimensionality reduction approach where,during an unsupervised multistage process,a feature set including high valuable markers is created which can facilitate the isolation of cell populations.Our proposed method,called fusion of the Spearman and Pearson affinity matrices(FSPAM),is based on a graph-based Gaussian kernel.Use of the graph theory can be effective to overcome the challenge of the nonlinear relations between cellular markers in scRNA-seq data.Furthermore,with a proper fusion of the Pearson and Spearman correlation coefficient criteria,it extracts a set of the most important features in a new space.In fact,the FSPAM aggregates the various aspects of cell-to-cell similarity derived from the Pearson and Spearman metrics,and reveals new aspects of cell-to-cell similarity,which can be used to extract new features.The results of the identification of cell populations via k-means++clustering method based on the features extracted from the FSPAM and different datasets of scRNA-seq suggested that the proposed method,regardless of the characteristics that govern each dataset,enjoys greater accuracy and better quality compared to previous methods.

Nano-design of ultra-efficient reversible block based on quantum-dot cellular automata

Reversible logic has recently gained significant interest due to its inherent ability to reduce energy dissipation,which is the primary need for low-power digital circuits.One of the newest areas of relevant study is reversible logic,which has applications in many areas,including nanotechnology,DNA computing,quantum computing,fault tolerance,and low-power complementary metal-oxide-semiconductor(CMOS).An electrical circuit is classified as reversible if it has an equal number of inputs and outputs,and a one-to-one relationship.A reversible circuit is conservative if the EXOR of the inputs and the EXOR of the outputs are equivalent.In addition,quantum-dot cellular automata(QCA)is one of the state-of-the-art approaches that can be used as an alternative to traditional technologies.Hence,we propose an efficient conservative gate with low power demand and high speed in this paper.First,we present a reversible gate called ANG(Ahmadpour Navimipour Gate).Then,two non-resistant QCA ANG and reversible fault-tolerant ANG structures are implemented in QCA technology.The suggested reversible gate is realized through the Miller algorithm.Subsequently,reversible fault-tolerant ANG is implemented by the 2DW clocking scheme.Furthermore,the power consumption of the suggested ANG is assessed under different energy ranges(0.5Ek,1.0Ek,and 1.5Ek).Simulations of the structures and analysis of their power consumption are performed using QCADesigner 2.0.03 and QCAPro software.The proposed gate shows great improvements compared to recent designs.

A robust intelligent audio watermarking scheme using support vector machine

信息技术和计算机网络的快速发展引发了数字域数据传输的广泛使用。然而,如何保护数据免于非授权复制与分发行为也是数据所有者们所面临的主要挑战。数字水印技术作为缓解导致系统效率下降潜在挑战一种可靠保护方法,逐渐被人们所认同数字音频水印应当能以人耳不能察觉的方式保持主信号的质量,也应当能在潜在的攻击前保持足够的稳健型。传统音频水印技术存在的一个主要问题是使用非智能解码器——此类解码器在提取数字水印时仅使用特定的规则集。本文提出了一种稳健、智能的音频水印方法,该方法有效地结合了奇异值分解(Singular value decomposition,SVD)和支持向量机(Support vector machine,SVM)技术。该方法通过调整奇异值实现水印数据嵌入,又通过SVM智能解码器实现水印提取。此外,通过学习噪声信号的有害效应,该解码器能够有效的提取水印。不同条件下的一系列实验验证了所述设计的性能。实验结果表明,与传统方法相比,本文方法能够提供更好的不可见性、更高的鲁棒性、更低的负载和更高的操作效率。