I will follow the video of Cherno to learn C++. The notes of the video will be organized into this blog series. The Cherno’s video is in here.

This blog is going to introduce how C++ works. How C++ process a source file (a text file) to an executable binary file. The workflow of writting a C++ program is:

series C++ source files –Compiler–> binary file

Let’s check it on Visual Studio (VS).

Polynomially bounded

Is the function ${ \lceil \lg n \rceil ! }$ polynomially bounded? Is the function ${ \lceil \lg \lg n \rceil !}$ polynomially bounded?

Proving that a funtion ${ f(n) }$ is polynomially bounded is equivalent to proving that ${ \lg (f(n)) = O(\lg n)}$.

This lecture is going to develop asymptotic notation mathematically and how to solve recurrences.

Merge Sort in C++

I code a C++ function to implement Merge Sort Alg, which, I know, is not the best way to do it but able to represent the whole idea of it. In fact, I met some troubles in the beginning, and I am confused to it. So I checked the memory and recorded the values and addresses of critical variables. So I draw the following figure to help us understand the Merge Sort Alg in detail and how C++ works when implementing it.

From today, I will post the study notes fo Linear Algebra (following the MIT 18.06). In the meantime, I am going to finish the assignments in this class and post my solutions.

The web page and videos are available on http://web.mit.edu/18.06/www/

The assignments are available on http://web.mit.edu/18.06/www/Fall2022/ and https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/pages/assignments/

From today, I will post my study notes of Introduction to Algorithms (following SMA 5503). In the meantime, I am going to finish the assignments in this book and post my solutions coded in both python and C++ (share in my github repository). Besides, I also want to become familiar with C++ during this process.

The spatial transcriptome is a novel and cutting-edge field and can identify single-celler transcriptome and corresponding locations. This paper reviews the current statistical and machine-learning methods in Spatial transcriptome. And it’s also my first paper read in this field. First, let us introduce the wet-lab technologies in this area to show what spatial transcriptome is.

我们之前以及讨论过Hifiasm这个算法，这是Heng Li发表在Nature Methods上的一篇关于单倍型重构的文章。其实，如果仅仅使用测序数据，我们很难实现telomere-to-telomere的单倍型组装，因为测序长度的限制，所以往往我们需要依靠额外的数据来进行phasing，比如hifiasm就采用了trio数据，即样本父母的二代测序信息。而这篇文章中，采用了Hi-C数据来作为额外的信息，这也是hifiasm(Hi-C)名字的来源。当然了，使用了Hi-C数据，就不再需要trio数据，也就是标题中的所提到的不需要父母数据。