design/tpl: Use \{emph=>dfn} for term introductions

This uses \textsl rather than \emph.
master
Mike Gerwitz 2021-05-18 12:16:11 -04:00
parent 8a2407d66f
commit dfa37f5b77
2 changed files with 13 additions and 13 deletions

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@ -1,7 +1,7 @@
\section{Classification System}\seclabel{class}
\index{classification}
A \emph{classification} is a user-defined abstraction that describes
A \dfn{classification} is a user-defined abstraction that describes
(``classifies'') arbitrary data.
Classifications can be used as predicates, generating functions, and can be
composed into more complex classifications.
@ -9,12 +9,12 @@ Nearly all conditions in \tame{} are specified using classifications.
\index{first-order logic!sentence}
\index{classification!coupling}
All classifications represent \emph{first-order sentences}---%
All classifications represent \dfn{first-order sentences}---%
that is,
they contain no \emph{free variables}.
they contain no \dfn{free variables}.
Intuitively,
this means that all variables within a~classification are
\emph{tightly coupled} to the classification itself.
\dfn{tightly coupled} to the classification itself.
This limitation is mitigated through use of the template system.
\begin{axiom}[Classification Introduction]\axmlabel{class-intro}
@ -74,7 +74,7 @@ A $\land$-classification is pronounced ``conjunctive classification'',
and $\lor$ ``disjunctive''.\footnote{%
Conjunctive and disjunctive classifications used to be referred to,
respectively,
as \emph{universal} and \emph{existential},
as \dfn{universal} and \dfn{existential},
referring to fact that
$\forall\Set{a_0,\ldots,a_n}(a) \equiv a_0\land\ldots\land a_n$,
and similarly for $\exists$.
@ -392,7 +392,7 @@ Then,
& \equiv \true.
\end{align*}
Each \xmlnode{match} of a classification is a~\emph{predicate}.
Each \xmlnode{match} of a classification is a~\dfn{predicate}.
Multiple predicates are by default joined by conjunction:
\begin{lstlisting}

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@ -231,7 +231,7 @@ For example,
\indexsym{[\,]}{function, image}
\index{function!image (\ensuremath{[\,]})}
\index{function!as a set}
The set of values over which some function~$f$ ranges is its \emph{image},
The set of values over which some function~$f$ ranges is its \dfn{image},
which is a subset of its codomain.
In the example above,
both the domain and codomain are the set of integers~$\Int$,
@ -253,9 +253,9 @@ We therefore have
\indexsym{()}{tuple}
\index{tuple (\ensuremath{()})}
\index{relation|see {function}}
An ordered pair $(x,y)$ is also called a \emph{$2$-tuple}.
An ordered pair $(x,y)$ is also called a \dfn{$2$-tuple}.
Generally,
an \emph{$n$-tuple} is used to represent an $n$-ary function,
an \dfn{$n$-tuple} is used to represent an $n$-ary function,
where by convention we have $(x)=x$.
So $f(x,y) = f((x,y)) = x+y$.
If we let $t=(x,y)$,
@ -264,7 +264,7 @@ If we let $t=(x,y)$,
necessary and where parenthesis may add too much noise;
this notation is especially well-suited to indexes,
as in $f_1$.
Binary functions are often written using \emph{infix} notation;
Binary functions are often written using \dfn{infix} notation;
for example, we have $x+y$ rather than $+(x,y)$.
\begin{equation}
@ -322,7 +322,7 @@ Given that, we have $f\bicomp{[]} = f\bicomp{[A]}$ for functions returning
\index{abstract algebra!monoid}
\index{monoid|see abstract algebra, monoid}
\begin{definition}[Monoid]\dfnlabel{monoid}
Let $S$ be some set. A \emph{monoid} is a triple $\Monoid S\bullet e$
Let $S$ be some set. A \dfn{monoid} is a triple $\Monoid S\bullet e$
with the axioms
\begin{align}
@ -438,7 +438,7 @@ A vector is a sequence of values, defined as a function of
\index{index set (\ensuremath{\Fam{a}jJ})}
\begin{definition}[Vector]\dfnlabel{vec}
Let $J\subset\Int$ represent an index set.
A \emph{vector}~$v\in\Vectors^\Real$ is a totally ordered sequence of
A \dfn{vector}~$v\in\Vectors^\Real$ is a totally ordered sequence of
elements represented as a function of an element of its index set:
\begin{equation}\label{vec}
v = \Vector{v_0,\ldots,v_j}^{\Real}_{j\in J}
@ -454,7 +454,7 @@ We may omit the superscript such that $\Vectors^\Real=\Vectors$
\index{vector!matrix}
\begin{definition}[Matrix]\dfnlabel{matrix}
Let $J\subset\Int$ represent an index set.
A \emph{matrix}~$M\in\Matrices$ is a totally ordered sequence of
A \dfn{matrix}~$M\in\Matrices$ is a totally ordered sequence of
elements represented as a function of an element of its index set:
\begin{equation}
M = \Vector{M_0,\ldots,M_j}^{\Vectors^\Real}_{j\in J}