summaryrefslogtreecommitdiff
path: root/macros/latex/contrib/thuthesis/data
diff options
context:
space:
mode:
authorNorbert Preining <norbert@preining.info>2020-06-09 03:01:30 +0000
committerNorbert Preining <norbert@preining.info>2020-06-09 03:01:30 +0000
commit03edc188c852d76ccf08759e0c334228f5f37c9f (patch)
tree08a3225e256e2bdf837eba0a45bf66b2d476f992 /macros/latex/contrib/thuthesis/data
parent7e1a772944cf3d35b15db92cf639c702d1acf053 (diff)
CTAN sync 202006090301
Diffstat (limited to 'macros/latex/contrib/thuthesis/data')
-rw-r--r--macros/latex/contrib/thuthesis/data/appendix-survey.tex52
-rw-r--r--macros/latex/contrib/thuthesis/data/appendix-translation.tex17
-rw-r--r--macros/latex/contrib/thuthesis/data/appendix.tex170
-rw-r--r--macros/latex/contrib/thuthesis/data/chap01.tex104
-rw-r--r--macros/latex/contrib/thuthesis/data/resume.tex2
5 files changed, 93 insertions, 252 deletions
diff --git a/macros/latex/contrib/thuthesis/data/appendix-survey.tex b/macros/latex/contrib/thuthesis/data/appendix-survey.tex
index 4a753f5211..f97039b45c 100644
--- a/macros/latex/contrib/thuthesis/data/appendix-survey.tex
+++ b/macros/latex/contrib/thuthesis/data/appendix-survey.tex
@@ -8,20 +8,24 @@
写出至少 5000 外文印刷字符的调研阅读报告或者书面翻译 1-2 篇(不少于 2 万外文印刷符)。
-It is impossible to cover in a single chapter every concept of mathematical
-programming.\cite{tex} This chapter introduces only the basic concepts and techniques of
-mathematical programming such that readers gain an understanding of them
-throughout the book\cite{abrahams99tex,salomon1995advanced}.
+
+\tableofcontents
\section{Single-Objective Programming}
+
+It is impossible to cover in a single chapter every concept of mathematical
+programming\cite{tex}. This chapter introduces only the basic concepts and techniques of
+mathematical programming such that readers gain an understanding of them
+throughout the book.~\cite{abrahams99tex,salomon1995advanced}.
+
The general form of single-objective programming (SOP) is written
as follows,
\begin{equation*} % 如果附录中的公式不想让它出现在公式索引中,那就请
% 用 equation*
\left\{\begin{array}{l}
-\max \,\,f(x)\\[0.1 cm]
-\mbox{subject to:} \\ [0.1 cm]
+\max \,\,f(x)\\%[0.1 cm]
+\mbox{subject to:} \\%[0.1 cm]
\qquad g_j(x)\le 0,\quad j=1,2,\cdots,p
\end{array}\right.
\end{equation*}
@@ -52,7 +56,7 @@ for any feasible solution $x$.
\end{mpdefop}
One of the outstanding contributions to mathematical programming was known as
-the Kuhn-Tucker conditions\ref{eq:ktc}. In order to introduce them, let us give
+the Kuhn-Tucker conditions~\eqref{eq:ktc}. In order to introduce them, let us give
some definitions. An inequality constraint $g_j(x)\le 0$ is said to be active at
a point $x^*$ if $g_j(x^*)=0$. A point $x^*$ satisfying $g_j(x^*)\le 0$ is said
to be regular if the gradient vectors $\nabla g_j(x)$ of all active constraints
@@ -65,34 +69,33 @@ $\lambda_j,j=1,2,\cdots,p$ such that the following Kuhn-Tucker conditions hold,
\begin{equation}
\label{eq:ktc}
\left\{\begin{array}{l}
- \nabla f(x^*)-\sum\limits_{j=1}^p\lambda_j\nabla g_j(x^*)=0\\[0.3cm]
- \lambda_jg_j(x^*)=0,\quad j=1,2,\cdots,p\\[0.2cm]
+ \nabla f(x^*)-\sum\limits_{j=1}^p\lambda_j\nabla g_j(x^*)=0\\%[0.3cm]
+ \lambda_jg_j(x^*)=0,\quad j=1,2,\cdots,p\\%[0.2cm]
\lambda_j\ge 0,\quad j=1,2,\cdots,p.
\end{array}\right.
\end{equation}
If all the functions $f(x)$ and $g_j(x),j=1,2,\cdots,p$ are convex and
differentiable, and the point $x^*$ satisfies the Kuhn-Tucker conditions
-(\ref{eq:ktc}), then it has been proved that the point $x^*$ is a global optimal
+\eqref{eq:ktc}, then it has been proved that the point $x^*$ is a global optimal
solution of SOP.
\subsection{Linear Programming}
\label{sec:lp}
If the functions $f(x),g_j(x),j=1,2,\cdots,p$ are all linear, then SOP is called
-a {\em linear programming}.
+a \emph{linear programming}.
The feasible set of linear is always convex. A point $x$ is called an extreme
point of convex set $S$ if $x\in S$ and $x$ cannot be expressed as a convex
combination of two points in $S$. It has been shown that the optimal solution to
linear programming corresponds to an extreme point of its feasible set provided
-that the feasible set $S$ is bounded. This fact is the basis of the {\em simplex
+that the feasible set $S$ is bounded. This fact is the basis of the \emph{simplex
algorithm} which was developed by Dantzig as a very efficient method for
solving linear programming.
\begin{table}[ht]
\centering
\centering
- \caption*{Table~1\hskip1em This is an example for manually numbered table, which
- would not appear in the list of tables}
+ \caption{This is an example for table}
\label{tab:badtabular2}
\begin{tabular}[c]{|m{1.5cm}|c|c|c|c|c|c|}\hline
\multicolumn{2}{|c|}{Network Topology} & \# of nodes &
@@ -118,7 +121,7 @@ extreme point is the optimal solution.
\subsection{Nonlinear Programming}
If at least one of the functions $f(x),g_j(x),j=1,2,\cdots,p$ is nonlinear, then
-SOP is called a {\em nonlinear programming}.
+SOP is called a \emph{nonlinear programming}.
A large number of classical optimization methods have been developed to treat
special-structural nonlinear programming based on the mathematical theory
@@ -126,8 +129,7 @@ concerned with analyzing the structure of problems.
\begin{figure}[h]
\centering
\includegraphics{thu-lib-logo.pdf}
- \caption*{Figure~1\quad This is an example for manually numbered figure,
- which would not appear in the list of figures}
+ \caption{This is an example for figure.}
\label{tab:badfigure2}
\end{figure}
@@ -141,8 +143,8 @@ points, each of which yields an improved objective function value over its
predecessor. The next point to be added to the sequence is chosen by analyzing
the behavior of the function at the previous points. This construction continues
until some termination criterion is met. Methods based upon this strategy are
-called {\em ascent methods}, which can be classified as {\em direct methods},
-{\em gradient methods}, and {\em Hessian methods} according to the information
+called \emph{ascent methods}, which can be classified as \emph{direct methods},
+\emph{gradient methods}, and \emph{Hessian methods} according to the information
about the behavior of objective function $f$. Direct methods require only that
the function can be evaluated at each point. Gradient methods require the
evaluation of first derivatives of $f$. Hessian methods require the evaluation
@@ -152,16 +154,16 @@ function.
\subsection{Integer Programming}
-{\em Integer programming} is a special mathematical programming in which all of
+\emph{Integer programming} is a special mathematical programming in which all of
the variables are assumed to be only integer values. When there are not only
-integer variables but also conventional continuous variables, we call it {\em
+integer variables but also conventional continuous variables, we call it \emph{
mixed integer programming}. If all the variables are assumed either 0 or 1,
-then the problem is termed a {\em zero-one programming}. Although integer
-programming can be solved by an {\em exhaustive enumeration} theoretically, it
+then the problem is termed a \emph{zero-one programming}. Although integer
+programming can be solved by an \emph{exhaustive enumeration} theoretically, it
is impractical to solve realistically sized integer programming problems. The
most successful algorithm so far found to solve integer programming is called
-the {\em branch-and-bound enumeration} developed by Balas (1965) and Dakin
-(1965). The other technique to integer programming is the {\em cutting plane
+the \emph{branch-and-bound enumeration} developed by Balas (1965) and Dakin
+(1965). The other technique to integer programming is the \emph{cutting plane
method} developed by Gomory (1959).
\hfill\textit{Uncertain Programming\/}\quad(\textsl{BaoDing Liu, 2006.2})
diff --git a/macros/latex/contrib/thuthesis/data/appendix-translation.tex b/macros/latex/contrib/thuthesis/data/appendix-translation.tex
index f085815300..5cc6549083 100644
--- a/macros/latex/contrib/thuthesis/data/appendix-translation.tex
+++ b/macros/latex/contrib/thuthesis/data/appendix-translation.tex
@@ -6,9 +6,12 @@
\title{书面翻译题目}
\maketitle
+\tableofcontents
+
+
\section{单目标规划}
北冥有鱼,其名为鲲。鲲之大,不知其几千里也。化而为鸟,其名为鹏。鹏之背,不知其几
-千里也。怒而飞,其翼若垂天之云。是鸟也,海运则将徙于南冥。南冥者,天池也。
+千里也。怒而飞,其翼若垂天之云。是鸟也,海运则将徙于南冥。南冥者,天池也。\cite{abrahams99tex}
\begin{equation}\tag*{(123)}
p(y|\mathbf{x}) = \frac{p(\mathbf{x},y)}{p(\mathbf{x})}=
\frac{p(\mathbf{x}|y)p(y)}{p(\mathbf{x})}
@@ -23,7 +26,7 @@
\begin{table}[ht]
\centering
\centering
- \caption*{表~1\hskip1em 这是手动编号但不出现在索引中的一个表格例子}
+ \caption{这是一个表格例子}
\label{tab:badtabular3}
\begin{tabular}[c]{|m{1.5cm}|c|c|c|c|c|c|}\hline
\multicolumn{2}{|c|}{Network Topology} & \# of nodes &
@@ -59,7 +62,7 @@
\begin{figure}[h]
\centering
\includegraphics{thu-whole-logo.pdf}
- \caption*{图~1\hskip1em 这是手动编号但不出现索引中的图片的例子}
+ \caption{这是图片的例子}
\label{tab:badfigure3}
\end{figure}
@@ -77,7 +80,7 @@
作孝弟,而侥幸于封侯富贵者也。子之罪大极重,疾走归!不然,我将以子肝益昼餔之膳。”
-\nocite{abrahams99tex,salomon1995advanced}
+% 书面翻译的参考文献
\bibliographystyle{plainnat}
\bibliography{ref/appendix}
@@ -92,5 +95,11 @@
% New York: Springer, 1995.
% \end{thebibliography}
+% 书面翻译对应的原文索引
+\begin{translation-index}
+ \nocite{salomon1995advanced}
+ \bibliographystyle{plainnat}
+ \bibliography{ref/appendix}
+\end{translation-index}
\end{translation}
diff --git a/macros/latex/contrib/thuthesis/data/appendix.tex b/macros/latex/contrib/thuthesis/data/appendix.tex
index 94a4a0595c..38e7548ba7 100644
--- a/macros/latex/contrib/thuthesis/data/appendix.tex
+++ b/macros/latex/contrib/thuthesis/data/appendix.tex
@@ -1,171 +1,5 @@
\chapter{单目标规划}
-As one of the most widely used techniques in operations
-research, \emph{ mathematical programming} is defined as a means of maximizing a
-quantity known as \emph{bjective function}, subject to a set of constraints
-represented by equations and inequalities. Some known subtopics of mathematical
-programming are linear programming, nonlinear programming, multiobjective
-programming, goal programming, dynamic programming, and multilevel
-programming.
-
-It is impossible to cover in a single chapter every concept of mathematical
-programming. This chapter introduces only the basic concepts and techniques of
-mathematical programming such that readers gain an understanding of them
-throughout the book.
-
-
-\section{Single-Objective Programming}
-The general form of single-objective programming (SOP) is written
-as follows,
-\begin{equation*} % 如果附录中的公式不想让它出现在公式索引中,那就请
- % 用 equation*
-\left\{\begin{array}{l}
-\max \,\,f(x)\\[0.1 cm]
-\mbox{subject to:} \\ [0.1 cm]
-\qquad g_j(x)\le 0,\quad j=1,2,\cdots,p
-\end{array}\right.
-\end{equation*}
-which maximizes a real-valued function $f$ of
-$x=(x_1,x_2,\cdots,x_n)$ subject to a set of constraints.
-
-\newcommand\Real{\mathbf{R}}
-\newtheorem{mpdef}{Definition}[chapter]
-\begin{mpdef}
-In SOP, we call $x$ a decision vector, and
-$x_1,x_2,\cdots,x_n$ decision variables. The function
-$f$ is called the objective function. The set
-\begin{equation*}
-S=\left\{x\in\Real^n\bigm|g_j(x)\le 0,\,j=1,2,\cdots,p\right\}
-\end{equation*}
-is called the feasible set. An element $x$ in $S$ is called a
-feasible solution.
-\end{mpdef}
-
-\newtheorem{mpdefop}[mpdef]{Definition}
-\begin{mpdefop}
-A feasible solution $x^*$ is called the optimal
-solution of SOP if and only if
-\begin{equation}
-f(x^*)\ge f(x)
-\end{equation}
-for any feasible solution $x$.
-\end{mpdefop}
-
-One of the outstanding contributions to mathematical programming was known as
-the Kuhn-Tucker conditions\ref{eq:ktc}. In order to introduce them, let us give
-some definitions. An inequality constraint $g_j(x)\le 0$ is said to be active at
-a point $x^*$ if $g_j(x^*)=0$. A point $x^*$ satisfying $g_j(x^*)\le 0$ is said
-to be regular if the gradient vectors $\nabla g_j(x)$ of all active constraints
-are linearly independent.
-
-Let $x^*$ be a regular point of the constraints of SOP and assume that all the
-functions $f(x)$ and $g_j(x),j=1,2,\cdots,p$ are differentiable. If $x^*$ is a
-local optimal solution, then there exist Lagrange multipliers
-$\lambda_j,j=1,2,\cdots,p$ such that the following Kuhn-Tucker conditions hold,
-\begin{equation}
-\label{eq:ktc}
-\left\{\begin{array}{l}
- \nabla f(x^*)-\sum\limits_{j=1}^p\lambda_j\nabla g_j(x^*)=0\\[0.3cm]
- \lambda_jg_j(x^*)=0,\quad j=1,2,\cdots,p\\[0.2cm]
- \lambda_j\ge 0,\quad j=1,2,\cdots,p.
-\end{array}\right.
-\end{equation}
-If all the functions $f(x)$ and $g_j(x),j=1,2,\cdots,p$ are convex and
-differentiable, and the point $x^*$ satisfies the Kuhn-Tucker conditions
-(\ref{eq:ktc}), then it has been proved that the point $x^*$ is a global optimal
-solution of SOP.
-
-\subsection{Linear Programming}
-\label{sec:lp}
-
-If the functions $f(x),g_j(x),j=1,2,\cdots,p$ are all linear, then SOP is called
-a {\em linear programming}.
-
-The feasible set of linear is always convex. A point $x$ is called an extreme
-point of convex set $S$ if $x\in S$ and $x$ cannot be expressed as a convex
-combination of two points in $S$. It has been shown that the optimal solution to
-linear programming corresponds to an extreme point of its feasible set provided
-that the feasible set $S$ is bounded. This fact is the basis of the {\em simplex
- algorithm} which was developed by Dantzig as a very efficient method for
-solving linear programming.
-\begin{table}[ht]
-\centering
- \centering
- \caption*{Table~1\hskip1em This is an example for manually numbered table, which
- would not appear in the list of tables}
- \label{tab:badtabular2}
- \begin{tabular}[c]{|m{1.5cm}|c|c|c|c|c|c|}\hline
- \multicolumn{2}{|c|}{Network Topology} & \# of nodes &
- \multicolumn{3}{c|}{\# of clients} & Server \\\hline
- GT-ITM & Waxman Transit-Stub & 600 &
- \multirow{2}{2em}{2\%}&
- \multirow{2}{2em}{10\%}&
- \multirow{2}{2em}{50\%}&
- \multirow{2}{1.2in}{Max. Connectivity}\\\cline{1-3}
- \multicolumn{2}{|c|}{Inet-2.1} & 6000 & & & &\\\hline
- \multirow{2}{1.5cm}{Xue} & Rui & Ni &\multicolumn{4}{c|}{\multirow{2}*{\thuthesis}}\\\cline{2-3}
- & \multicolumn{2}{c|}{ABCDEF} &\multicolumn{4}{c|}{} \\\hline
-\end{tabular}
-\end{table}
-
-Roughly speaking, the simplex algorithm examines only the extreme points of the
-feasible set, rather than all feasible points. At first, the simplex algorithm
-selects an extreme point as the initial point. The successive extreme point is
-selected so as to improve the objective function value. The procedure is
-repeated until no improvement in objective function value can be made. The last
-extreme point is the optimal solution.
-
-\subsection{Nonlinear Programming}
-
-If at least one of the functions $f(x),g_j(x),j=1,2,\cdots,p$ is nonlinear, then
-SOP is called a {\em nonlinear programming}.
-
-A large number of classical optimization methods have been developed to treat
-special-structural nonlinear programming based on the mathematical theory
-concerned with analyzing the structure of problems.
-\begin{figure}[h]
- \centering
- \includegraphics{thu-lib-logo.pdf}
- \caption*{Figure~1\quad This is an example for manually numbered figure,
- which would not appear in the list of figures}
- \label{tab:badfigure2}
-\end{figure}
-
-Now we consider a nonlinear programming which is confronted solely with
-maximizing a real-valued function with domain $\Real^n$. Whether derivatives are
-available or not, the usual strategy is first to select a point in $\Real^n$ which
-is thought to be the most likely place where the maximum exists. If there is no
-information available on which to base such a selection, a point is chosen at
-random. From this first point an attempt is made to construct a sequence of
-points, each of which yields an improved objective function value over its
-predecessor. The next point to be added to the sequence is chosen by analyzing
-the behavior of the function at the previous points. This construction continues
-until some termination criterion is met. Methods based upon this strategy are
-called {\em ascent methods}, which can be classified as {\em direct methods},
-{\em gradient methods}, and {\em Hessian methods} according to the information
-about the behavior of objective function $f$. Direct methods require only that
-the function can be evaluated at each point. Gradient methods require the
-evaluation of first derivatives of $f$. Hessian methods require the evaluation
-of second derivatives. In fact, there is no superior method for all
-problems. The efficiency of a method is very much dependent upon the objective
-function.
-
-\subsection{Integer Programming}
-
-{\em Integer programming} is a special mathematical programming in which all of
-the variables are assumed to be only integer values. When there are not only
-integer variables but also conventional continuous variables, we call it {\em
- mixed integer programming}. If all the variables are assumed either 0 or 1,
-then the problem is termed a {\em zero-one programming}. Although integer
-programming can be solved by an {\em exhaustive enumeration} theoretically, it
-is impractical to solve realistically sized integer programming problems. The
-most successful algorithm so far found to solve integer programming is called
-the {\em branch-and-bound enumeration} developed by Balas (1965) and Dakin
-(1965). The other technique to integer programming is the {\em cutting plane
- method} developed by Gomory (1959).
-
-\hfill\textit{Uncertain Programming\/}\quad(\textsl{BaoDing Liu, 2006.2})
-
\section{单目标规划}
北冥有鱼,其名为鲲。鲲之大,不知其几千里也。化而为鸟,其名为鹏。鹏之背,不知其几
千里也。怒而飞,其翼若垂天之云。是鸟也,海运则将徙于南冥。南冥者,天池也。
@@ -183,7 +17,7 @@ the {\em branch-and-bound enumeration} developed by Balas (1965) and Dakin
\begin{table}[ht]
\centering
\centering
- \caption*{表~1\hskip1em 这是手动编号但不出现在索引中的一个表格例子}
+ \caption{这是一个表格例子}
\label{tab:badtabular3}
\begin{tabular}[c]{|m{1.5cm}|c|c|c|c|c|c|}\hline
\multicolumn{2}{|c|}{Network Topology} & \# of nodes &
@@ -219,7 +53,7 @@ the {\em branch-and-bound enumeration} developed by Balas (1965) and Dakin
\begin{figure}[h]
\centering
\includegraphics{thu-whole-logo.pdf}
- \caption*{图~1\hskip1em 这是手动编号但不出现索引中的图片的例子}
+ \caption{这是图片的例子}
\label{tab:badfigure3}
\end{figure}
diff --git a/macros/latex/contrib/thuthesis/data/chap01.tex b/macros/latex/contrib/thuthesis/data/chap01.tex
index e1ab5e19d8..01096ad170 100644
--- a/macros/latex/contrib/thuthesis/data/chap01.tex
+++ b/macros/latex/contrib/thuthesis/data/chap01.tex
@@ -43,23 +43,11 @@
知周乎万物,而道济天下,故不过。旁行而不流,乐天知命,故不忧。安土敦乎仁,故
能爱。范围天地之化而不过,曲成万物而不遗,通乎昼夜之道而知,故神无方而易无体。}
-% 非本科生一般用不到幼圆与隶书字体。需要的同学请查看 ctex 文档。
-{\ifcsname youyuan\endcsname\youyuan\else[无 \cs{youyuan} 字体。]\fi 有天地,然后
- 万物生焉。盈天地之间者,唯万物,故受之以屯;屯者盈也,屯者物之始生也。物生必蒙,
- 故受之以蒙;蒙者蒙也,物之穉也。物穉不可不养也,故受之以需;需者饮食之道也。饮
- 食必有讼,故受之以讼。讼必有众起,故受之以师;师者众也。众必有所比,故受之以比;
- 比者比也。比必有所畜也,故受之以小畜。物畜然后有礼,故受之以履。}
-
{\heiti 履而泰,然后安,故受之以泰;泰者通也。物不可以终通,故受之以否。物不可以终
否,故受之以同人。与人同者,物必归焉,故受之以大有。有大者不可以盈,故受之以谦。
有大而能谦,必豫,故受之以豫。豫必有随,故受之以随。以喜随人者,必有事,故受
之以蛊;蛊者事也。}
-{\ifcsname lishu\endcsname\lishu\else[无 \cs{lishu} 字体。]\fi 有事而后可大,故受
- 之以临;临者大也。物大然后可观,故受之以观。可观而后有所合,故受之以噬嗑;嗑者
- 合也。物不可以苟合而已,故受之以贲;贲者饰也。致饰然后亨,则尽矣,故受之以剥;
- 剥者剥也。物不可以终尽,剥穷上反下,故受之以复。复则不妄矣,故受之以无妄。}
-
{\songti 有无妄然后可畜,故受之以大畜。物畜然后可养,故受之以颐;颐者养也。不养则不
可动,故受之以大过。物不可以终过,故受之以坎;坎者陷也。陷必有所丽,故受之以
离;离者丽也。}
@@ -76,23 +64,28 @@
配合使用。如果表格比较简单的话可以直接用命令 \cs{hlinewd}\marg{width} 控制。
\begin{table}[htb]
\centering
- \begin{minipage}[t]{0.8\linewidth} % 如果想在表格中使用脚注,minipage是个不错的办法
+ \begin{threeparttable}[b]
\caption[模板文件]{模板文件。如果表格的标题很长,那么在表格索引中就会很不美
观,所以要像 chapter 那样在前面用中括号写一个简短的标题。这个标题会出现在索
引中。}
\label{tab:template-files}
- \begin{tabularx}{\linewidth}{lX}
- \toprule[1.5pt]
- {\heiti 文件名} & {\heiti 描述} \\\midrule[1pt]
- thuthesis.ins & \LaTeX{} 安装文件,DocStrip\footnote{表格中的脚注} \\
- thuthesis.dtx & 所有的一切都在这里面\footnote{再来一个}。\\
+ \begin{tabular}{ll}
+ \toprule
+ {\heiti 文件名} & {\heiti 描述} \\\midrule
+ thuthesis.ins & \LaTeX{} 安装文件,DocStrip。\tnote{1} \\
+ thuthesis.dtx & 所有的一切都在这里面。\tnote{b}\\
thuthesis.cls & 模板类文件。\\
thuthesis-numeric.bst & 参考文献 BibTeX 样式文件。\\
thuthesis-author-year.bst & 参考文献 BibTeX 样式文件。\\
thusetup.tex & 基本配置和常用的包、命令,减轻主文件的负担。\\
- \bottomrule[1.5pt]
- \end{tabularx}
- \end{minipage}
+ \bottomrule
+ \end{tabular}
+ \begin{tablenotes}
+ \item [1] 表格中的脚注。如果脚注比较长,那么换行后的效果是这样的,需要更多的文字。
+ 现在能看到么?
+ \item [b] 再来一个。可以使用不同的 symbol。
+ \end{tablenotes}
+ \end{threeparttable}
\end{table}
首先来看一个最简单的表格。表 \ref{tab:template-files} 列举了本模板主要文件及其功
@@ -153,9 +146,9 @@
\caption{第一个并排子表格}
\label{tab:parallel1}
\begin{tabular}{p{2cm}p{2cm}}
-\toprule[1.5pt]
-111 & 222 \\\midrule[1pt]
-222 & 333 \\\bottomrule[1.5pt]
+\toprule
+111 & 222 \\\midrule
+222 & 333 \\\bottomrule
\end{tabular}
\end{minipage}%
\begin{minipage}{0.5\textwidth}
@@ -163,9 +156,9 @@
\caption{第二个并排子表格}
\label{tab:parallel2}
\begin{tabular}{p{2cm}p{2cm}}
-\toprule[1.5pt]
-111 & 222 \\\midrule[1pt]
-222 & 333 \\\bottomrule[1.5pt]
+\toprule
+111 & 222 \\\midrule
+222 & 333 \\\bottomrule
\end{tabular}
\end{minipage}
\end{table}
@@ -192,18 +185,18 @@
\subcaptionbox{第一个子表格}
{
\begin{tabular}{p{2cm}p{2cm}}
-\toprule[1.5pt]
-111 & 222 \\\midrule[1pt]
-222 & 333 \\\bottomrule[1.5pt]
+\toprule
+111 & 222 \\\midrule
+222 & 333 \\\bottomrule
\end{tabular}
}
\hskip2cm
\subcaptionbox{第二个子表格}
{
\begin{tabular}{p{2cm}p{2cm}}
-\toprule[1.5pt]
-111 & 222 \\\midrule[1pt]
-222 & 333 \\\bottomrule[1.5pt]
+\toprule
+111 & 222 \\\midrule
+222 & 333 \\\bottomrule
\end{tabular}
}
\end{table}
@@ -242,20 +235,20 @@
\begin{longtable}[c]{c*{6}{r}}
\caption{实验数据}\label{tab:performance}\\
-\toprule[1.5pt]
+\toprule
测试程序 & \multicolumn{1}{c}{正常运行} & \multicolumn{1}{c}{同步} & \multicolumn{1}{c}{检查点} & \multicolumn{1}{c}{卷回恢复}
& \multicolumn{1}{c}{进程迁移} & \multicolumn{1}{c}{检查点} \\
& \multicolumn{1}{c}{时间 (s)}& \multicolumn{1}{c}{时间 (s)}&
\multicolumn{1}{c}{时间 (s)}& \multicolumn{1}{c}{时间 (s)}& \multicolumn{1}{c}{
- 时间 (s)}& 文件(KB)\\\midrule[1pt]
+ 时间 (s)}& 文件(KB)\\\midrule
\endfirsthead
\multicolumn{7}{c}{续表~\thetable\hskip1em 实验数据}\\
-\toprule[1.5pt]
+\toprule
测试程序 & \multicolumn{1}{c}{正常运行} & \multicolumn{1}{c}{同步} & \multicolumn{1}{c}{检查点} & \multicolumn{1}{c}{卷回恢复}
& \multicolumn{1}{c}{进程迁移} & \multicolumn{1}{c}{检查点} \\
& \multicolumn{1}{c}{时间 (s)}& \multicolumn{1}{c}{时间 (s)}&
\multicolumn{1}{c}{时间 (s)}& \multicolumn{1}{c}{时间 (s)}& \multicolumn{1}{c}{
- 时间 (s)}& 文件(KB)\\\midrule[1pt]
+ 时间 (s)}& 文件(KB)\\\midrule
\endhead
\hline
\multicolumn{7}{r}{续下页}
@@ -285,7 +278,7 @@ EP.A.8 & 31.06 & 0.004 & 0.017 & 0.005 & 0.073 & 1661 \\
EP.B.2 & 495.49 & 0.001 & 0.009 & 0.003 & 0.196 & 2011 \\
EP.B.4 & 247.69 & 0.002 & 0.012 & 0.004 & 0.122 & 1663 \\
EP.B.8 & 126.74 & 0.003 & 0.017 & 0.005 & 0.083 & 1656 \\
-\bottomrule[1.5pt]
+\bottomrule
\end{longtable}
要想用好论文模板还是得提前学习一些 \TeX/\LaTeX{} 的相关知识,具备一些基本能力,掌
@@ -343,7 +336,7 @@ EP.B.8 & 126.74 & 0.003 & 0.017 & 0.005 & 0.083 & 1656 \\
\section{定理环境}
\label{sec:theorem}
-给大家演示一下各种和证明有关的环境:
+给大家演示一下各种和证明有关的环境,比如公式~\eqref{eq:eqnxmp}:
\begin{assumption}
待月西厢下,迎风户半开;隔墙花影动,疑是玉人来。
@@ -358,6 +351,7 @@ EP.B.8 & 126.74 & 0.003 & 0.017 & 0.005 & 0.083 & 1656 \\
何反起悲伤?(引自《杜十娘怒沉百宝箱》)
\begin{definition}
+\label{def:ziyue}
子曰:「道千乘之国,敬事而信,节用而爱人,使民以时。」
\end{definition}
@@ -365,15 +359,14 @@ EP.B.8 & 126.74 & 0.003 & 0.017 & 0.005 & 0.083 & 1656 \\
君应有语,渺万里层云,千山暮雪,只影向谁去?
横汾路,寂寞当年箫鼓,荒烟依旧平楚。招魂楚些何嗟及,山鬼暗谛风雨。天也妒,未信与,莺儿燕子俱黄土。
-千秋万古,为留待骚人,狂歌痛饮,来访雁丘处。
+千秋万古,为留待骚人,狂歌痛饮,来访雁丘处。(参见定义~\ref{def:ziyue})
\begin{proposition}
曾子曰:「吾日三省吾身 —— 为人谋而不忠乎?与朋友交而不信乎?传不习乎?」
\end{proposition}
-多么凄美的命题啊!其日牛马嘶,新妇入青庐,奄奄黄昏后,寂寂人定初,我命绝今日,
-魂去尸长留,揽裙脱丝履,举身赴清池,府吏闻此事,心知长别离,徘徊庭树下,自挂东南
-枝。
+其日牛马嘶,新妇入青庐,奄奄黄昏后,寂寂人定初,我命绝今日,魂去尸长留,揽裙脱丝履,举身赴清池,
+府吏闻此事,心知长别离,徘徊庭树下,自挂东南枝。
\begin{remark}
天不言自高,水不言自流。
@@ -430,12 +423,15 @@ x&\equiv y+1\pod{m^2}
\begin{theorem}\label{the:theorem1}
犯我强汉者,虽远必诛\hfill —— 陈汤(汉)
\end{theorem}
+
+下面演示了子公式编号:
\begin{subequations}
\begin{align}
y & = 1 \\
y & = 0
\end{align}
\end{subequations}
+
道可道,非常道。名可名,非常名。无名天地之始;有名万物之母。故常无,欲以观其妙;
常有,欲以观其徼。此两者,同出而异名,同谓之玄。玄之又玄,众妙之门。上善若水。水
善利万物而不争,处众人之所恶,故几于道。曲则全,枉则直,洼则盈,敝则新,少则多,
@@ -521,17 +517,17 @@ V_j & = v_j, & \qquad X_j & = x_j,
有时候不想要上标,那么可以这样~\inlinecite{shaheshang},这个非常重要。
-有时候一些参考文献没有纸质出处,需要标注 URL。缺省情况下,URL 不会在连字符处断行,
-这可能使得用连字符代替空格的网址分行很难看。如果需要,可以将模板类文件中
-\begin{verbatim}
-\RequirePackage{hyperref}
-\end{verbatim}
-一行改为:
-\begin{verbatim}
-\PassOptionsToPackage{hyphens}{url}
-\RequirePackage{hyperref}
-\end{verbatim}
-使得连字符处可以断行。更多设置可以参考 \texttt{url} 宏包文档。
+% 有时候一些参考文献没有纸质出处,需要标注 URL。缺省情况下,URL 不会在连字符处断行,
+% 这可能使得用连字符代替空格的网址分行很难看。如果需要,可以将模板类文件中
+% \begin{verbatim}
+% \RequirePackage{hyperref}
+% \end{verbatim}
+% 一行改为:
+% \begin{verbatim}
+% \PassOptionsToPackage{hyphens}{url}
+% \RequirePackage{hyperref}
+% \end{verbatim}
+% 使得连字符处可以断行。更多设置可以参考 \texttt{url} 宏包文档。
\section{公式}
\label{sec:equation}
diff --git a/macros/latex/contrib/thuthesis/data/resume.tex b/macros/latex/contrib/thuthesis/data/resume.tex
index 80bd6aa06b..587d9ef628 100644
--- a/macros/latex/contrib/thuthesis/data/resume.tex
+++ b/macros/latex/contrib/thuthesis/data/resume.tex
@@ -25,7 +25,7 @@
% 2. 尚未刊载,但已经接到正式录用函的学术论文(本人为第一作者,或者
% 导师为第一作者本人是第二作者)。
- \begin{publications}[before=\publicationskip,after=\publicationskip]
+ \begin{publications}[before*=\publicationskip,after*=\publicationskip]
\item Yang Y, Ren T L, Zhu Y P, et al. PMUTs for handwriting recognition. In
press. (已被 Integrated Ferroelectrics 录用. SCI 源刊.)
\end{publications}