single-number

本文介绍了一种使用异或操作解决寻找数组中唯一出现一次元素的方法。该算法具备线性时间复杂度且不使用额外内存,符合高效查找的要求。

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题目描述

Given an array of integers, every element appears twice except for one. Find that single one.
Note:

Your algorithm should have a linear runtime complexity. Could you implement it without using extra memory?

IDEA

用异或。二进制异或,两位相同为0,不同得1。即两个数相同,异或得0. 0和任何数异或得到该数本身。 将数组中所有数进行异或,其中出现两次的数抵消为0,剩下的就是出现一次的那个数。另外,我的解法没有开辟另外的存储空间

CODE

class Solution {
public:
    int singleNumber(int A[], int n) {
       
        for(int i=1;i<n;i++){
            A[0]^=A[i];
        }
        return A[0];
    }
};


STELLAR (Spatially-resolved Transcriptomics with Ellipsoid Decomposition and Latent Actualization for Reconstruction) is a computational tool developed by researchers at the Broad Institute of MIT and Harvard for annotating spatially resolved single-cell data. It uses a combination of machine learning algorithms and image analysis techniques to identify cell types and characterize gene expression patterns within individual cells. To use STELLAR, researchers first generate spatially resolved single-cell data using techniques such as spatial transcriptomics or in situ sequencing. This data typically consists of spatial coordinates for each cell, as well as information on gene expression levels for a large number of genes. STELLAR then uses a number of different algorithms to analyze this data and identify cell types. First, it uses an ellipsoid decomposition algorithm to model the spatial distribution of cells within the tissue sample. This allows it to identify clusters of cells that are likely to be of the same type. Next, STELLAR uses a latent actualization algorithm to model the gene expression patterns within each cell. This allows it to identify genes that are expressed at high levels within specific cell types, and to assign cell type labels to individual cells based on their gene expression profiles. Overall, STELLAR provides a powerful tool for analyzing spatially resolved single-cell data, and has the potential to significantly advance our understanding of cellular organization and function within complex tissues.
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