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text:
\section{Introduction}
Over several decades, continued advances in electrical impedance tomography (EIT) have expanded the clinical capability of real-time cardiopulmonary monitoring systems by overcoming the limitations of traditional methods, such as cardiac catheterization through blood vessels \cite{Adler2017,Borges2012,Deibele2008,Frerichs2014,Kubicek1970,Kerrouche2001,Lee2018,Putensen2019,Zlochiver2006}. Recently, based on thoracic EIT, a patient's hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a signal extracted using EIT, the so-called cardiac volume signal (CVS), which has a strong relationship with key hemodynamic factors such as stroke volume and cardiac output \cite{Jang2020,Askari2019,Westterhof2019}. In clinical applications, however, a cardiac volume signal is often of low quality, mainly because of the patient's deliberate movements or inevitable motions during clinical interventions such as medical treatment and nursing. Because postural change causes movement of the chest boundary to which existing EIT solvers are highly sensitive owing to time-difference-reconstruction characteristics \cite{Adler1996,Boyle2010,Lionheart1998,Lee2017,Seo2013}, motion-induced artifacts are generated in the CVS, as shown in Figure \ref{CVSmotionartifacts}.

\begin{figure}[h]
	\centering
	\includegraphics[width=0.65\textwidth]{fig1.pdf}
	\caption{Motion-induced artifacts in cardiac volume monitoring using electrical impedance tomography. Patient's deliberate movements or inevitable motions during clinical intervention cause severe artifacts in a cardiac volume signal.}
	\label{CVSmotionartifacts}
\end{figure}

CVS extraction is to separate a cardiogenic component from the EIT voltage data, resulting from current injections at electrodes attached across a human chest. In recent studies \cite{Jang2020,Lee2021}, effective CVS extraction was successful in motion-free measurements where voltage data are mainly influenced by air and blood volume changes in the lungs, heart, and blood vessels comprehensively, but not by motions. In contrast, achieving the cardiogenic component separation in motion-influenced measurements is still a long-term challenge. Postural changes in EIT measurements cause strong distortion of the voltage data \cite{Adler1996,Zhang2005} and easily disturb the extraction of relatively weak cardiogenic signals \cite{Brown1992,Pikkemaat2014,Leonhardt2012}.

Handling motion interference has been a huge challenge in most EIT-based techniques for enhancing clinical capability, but not researched much yet \cite{Yang2022}. Adler \textit{et al.} \cite{Adler1996} and Zhang \textit{et al.} \cite{Zhang2005} investigated the negative motion effect in the EIT. Soleimani \textit{et al.} \cite{Soleimani2006} and Dai \textit{et al.} \cite{Dai2008} proposed a motion-induced artifact reduction method by reconstructing electrode movements along with conductivity changes. Lee \textit{et al.} \cite{Lee2017} analyzed motion artifacts in EIT measurements and proposed a subspace-based artifact rejection method. Yang \textit{et al.} \cite{Yang2022} suggested the discrete wavelet transform-based approach that reduces motion artifacts of three specific types. However, clinical motion artifacts are still not effectively addressed because of practical motion's immense diversity and complexity. Accordingly, for the time being, the EIT-based hemodynamic monitoring system attempts to be preferentially developed toward filtering motion-influenced CVSs rather than recovering them. In the clinical environment, this filtration can provide immediate warnings so that clinicians can minimize confusion regarding the patient's condition, reduce clinical resource utilization, and improve the confidence level of the monitoring system \cite{Charlton2021}.

This study aims to develop a signal quality indexing (SQI) method that assesses whether motion artifacts influence transient CVSs. To take advantage of the periodicity and regularity in cardiac volume changes, the assessment is performed on each cardiac cycle, whose time intervals are identified using the synchronized electrocardiography (ECG) system. We leverage machine learning (ML), which has provided effective solutions for various biosignal-related tasks through feature disentanglement of complicated signals \cite{Alfaras2019,Belo2017,Celin2018,Hyun2021,Lecun2015,Seo2019,Sahoo2020,Wasimuddin2020}. The use of ML could be suitable for our real-time monitoring application that requires fast inference and automation as well as high accuracy.

We apply divergent ML methods, which can be sorted into discriminative-model and manifold-learning approaches. The discriminative-model approach is first considered, where an SQI map is directly trained using a paired dataset of CVS and its label \cite{Cramer2002,Hinton2007,Simonyan2014}. Although this approach provides a high performance on a fixed dataset, owing to the class imbalance problem, there is a risk of overfitting on motion-influenced CVS data in the scope of generalization or stability \cite{Buda2018,Cao2019,He2009,Van2017}. Motion artifacts can vary considerably in real circumstances, whereas collecting CVS data in numerous motion-influenced cases is practically limited because of the high cost, intensive labor, security, and ambiguity in clinical data acquisition and annotation \cite{Chapelle2009,Schlegl2017,Van2020,Zhu2009}. To handle this conceivable difficulty, the manifold-learning approach \cite{Jolliffe2016,Kingma2013,An2015,Higgins2016} is examined as an alternative. It does not learn irregular and capricious patterns of motion-influenced CVSs and only takes advantage of the learned features from motion-free CVSs.

Numerous experiments have been conducted using actual EIT data. Empirical results demonstrate that discriminative and manifold-learning models provide accurate and automatic detection of motion-influenced CVS in real-time. The best discriminative model achieved an accuracy of 0.95, positive and negative predictive values of 0.96 and 0.86, sensitivity of 0.98, specificity of 0.77, and AUC of 0.96. The best manifold-learning model achieved accuracy of 0.93, positive and negative predictive values of 0.97 and 0.71, sensitivity of 0.95, specificity of 0.80, and AUC of 0.95. The discriminative models yielded a more powerful SQI performance; in contrast, the manifold-learning models provided stable outcomes between the training and test sets. Regarding to practical applications, the choice of two models relies on what should be emphasized in the monitoring system in terms of performance and stability.

\section{Methods}
\begin{figure}[h]
	\centering
	\includegraphics[width=1\textwidth]{fig2}
	\caption{16-channel system of thoracic EIT and CVS extraction. The EIT machine measures voltage differences by injecting currents via electrodes attached along human chest. A cardiac volume signal $\boldsymbol x_{\boldsymbol t}$ is extracted by taking suitable weighting $\boldsymbol w$ to the time-difference transconductance $\dot{\boldsymbol g}_t$, which is defined by measured voltage data. Here, $\boldsymbol w$ is called as a leadforming vector, which is designed to separate a cardiogenic trans-conductance change from superposed data $\dot{\boldsymbol g}_t$ \cite{Lee2021}.}
	\label{EItsystem}
\end{figure}

This study considers the 16-channel system of the thoracic EIT, where 16 electrodes are attached along the human chest (see Figure \ref{EItsystem}). The EIT system is assumed to be synchronized with the ECG system, which provides the time interval for each cardiac cycle. The EIT device measures a set of voltage differences by injecting an alternative current of $I$ (mA) through pairs of adjacent electrodes while keeping all other electrodes insulated. At sampling time $t$, the following voltages are acquired:
\begin{equation} \label{voltagedata}
	\{ V^{j,k}_{t} ~ : ~ V^{j,k}_t =  U_t^{j,k} - U_t^{j,k+1}, j \in \mathcal I, k \in \mathcal I \backslash \{j,j+1\} \}
\end{equation}
where $\mathcal I$ is an index set defined by $\mathcal I = \{1,2,\cdots,16\}$, $\mathcal E_k$ is the $k$-th electrode, and $U_t^{j,k}$ is the electrical potential on $\mathcal E_k$ subject to the current injection from $\mathcal E_j$ to $\mathcal E_{j+1}$. For notational convenience, $\mathcal E^{0}$ and $\mathcal E^{17}$ can be understood as $\mathcal E^{16}$ and $\mathcal E^{1}$, respectively. Once the current is injected from $\mathcal E^{j}$ to $\mathcal E^{j+1}$ for some $j \in \mathcal I$, the voltage is measured at each of the 16 adjacent electrode pairs $(\mathcal E^k, \mathcal E^{k+1})_{k \in \mathcal I}$. Among the 16 voltages, $V_t^{j,j-1}$, $V_t^{j,j}$, and $V_t^{j,j+1}$ are discarded to reduce the influence of the skin-electrode contact impedance \cite{Seo2013}. Because we perform 16 independent current injections, in total, $208$ $(= 16 \times 13)$ voltages are obtained and used to produce the CVS.

\subsection{CVS Extraction Using EIT and Influence of Motion}
A transconductance (column) vector $\boldsymbol g_t \in \mathbb{R}^{208}$ can be defined using the voltage data \eqref{voltagedata} as follows:
\begin{equation}\label{conductance}
	{\footnotesize \boldsymbol g_t = \begin{bmatrix} \dfrac{I}{\mathfrak R(V^{1,3}_t)}, \cdots, \dfrac{I}{\mathfrak R(V^{1,15}_t)}, \cdots, \dfrac{I}{\mathfrak R(V^{16,2}_t)},\cdots  \dfrac{I}{\mathfrak R(V^{16,14}_t)} \end{bmatrix}^T}
\end{equation}
where $T$ represents the vector transpose and $\mathfrak R$ is an operation for extracting the real part of a complex number. Here, $\boldsymbol g_t$ is updated every 10ms.

A CVS, denoted by $\boldsymbol x_t \in \mathbb{R}$, is obtained by
\begin{equation}
	\boldsymbol x_t = \boldsymbol w^T \dot{\boldsymbol g_t}
\end{equation}
where $\boldsymbol w \in \mathbb{R}^{208}$ is a weighting (so-called leadforming) vector and $\dot{\boldsymbol g_t}$ is time difference of $\boldsymbol g_t$ given by
\begin{equation}
	\dot{\boldsymbol g}_t = \boldsymbol g_t - \boldsymbol g_{t_0} \mbox{ for reference time } t_0
\end{equation}
In the absence of motion, the transconductance $\dot{\boldsymbol g}_t$ can be expressed by
\begin{equation}\label{noMAcondi}
	\dot{\boldsymbol g}_t = \dot{\boldsymbol g}^{\mbox{\scriptsize air}}_t + \dot{\boldsymbol g}^{\mbox{\scriptsize blood}}_t
\end{equation}
where ${\boldsymbol g}^{\mbox{\scriptsize air}}_t$ and ${\boldsymbol g}^{\mbox{\scriptsize blood}}_t$ are transconductance vectors related to air and blood volume changes in the lungs and heart, respectively. The weighting vector $\boldsymbol w$ is designed to provide
\begin{equation}\label{LFcondi}
	\boldsymbol w^T \dot{\boldsymbol g}_t =  \boldsymbol w^T(\dot{\boldsymbol g}^{\mbox{\scriptsize air}}_t + \dot{\boldsymbol g}^{\mbox{\scriptsize blood}}_t)= \boldsymbol w^T \dot{\boldsymbol g}^{\mbox{\scriptsize blood}}_t
\end{equation}
See Figure \ref{EItsystem}. Kindly refer to \cite{Lee2021} for details on determining $\boldsymbol w$. Even though the cardiogenic signal ${\boldsymbol g}^{\mbox{\scriptsize blood}}_t$ is weak, it can be accurately decomposed from the data $\boldsymbol g_t$.

In light of the previous analysis in \cite{Lee2017}, the following explains why the quality of the CVS is degraded by motion, as shown in the middle part of Figure \ref{CVSmotionartifacts}. In the presence of motion, the transconductance $\dot{\boldsymbol g}_t$ can be approximated by
\begin{equation}\label{motionartifacts}
	\dot{\boldsymbol g}_t \approx \dot{\boldsymbol g}_t^{\mbox{\scriptsize normal}} + \dot{\boldsymbol g}^{\mbox{\scriptsize motion}}_t
\end{equation}
where $\dot{\boldsymbol g}^{\mbox{\scriptsize normal}}_t = \dot{\boldsymbol g}^{\mbox{\scriptsize air}}_t + \dot{\boldsymbol g}^{\mbox{\scriptsize blood}}_t$ and $\dot{\boldsymbol g}^{\mbox{\scriptsize motion}}_t$ is the motion-induced effect. Appendix \ref{appendixA} presents details of \eqref{motionartifacts}. Determining the vector $\boldsymbol w$ itself can be considerably affected by motion artifacts \cite{Lee2021}. Moreover, even if $\boldsymbol w$ satisfies \eqref{LFcondi}, we have
\begin{equation}
	\boldsymbol x_t = \boldsymbol w^T \dot{\boldsymbol g}_t \approx \boldsymbol x_t^{\mbox{\scriptsize normal}} + \boldsymbol x_t^{\mbox{\scriptsize motion}}
\end{equation}
where $\boldsymbol x_t^{\mbox{\scriptsize normal}} = \boldsymbol w^T \dot{\boldsymbol g}_t^{\mbox{\scriptsize blood}}$ and $\boldsymbol x_t^{\mbox{\scriptsize motion}} = \boldsymbol w^T \dot{\boldsymbol g}_t^{\mbox{\scriptsize motion}}$. The last term $\boldsymbol x_t^{\mbox{\scriptsize motion}}$ describes motion artifacts in the CVS.

\begin{figure}[h]
	\centering
	\includegraphics[width=1\textwidth]{fig3.pdf}
	\caption{Schematic description of machine learning-based signal quality assessment for cardiac volume monitoring in electrical impedance tomography.}
	\label{mainfig}
\end{figure}

\subsection{CVS Quality Assessment and Data Preprocessing} \label{DataPre}
This study aims to assess the CVS ($\boldsymbol x_t$) for detecting motion-induced signal quality degradation. See Figure \ref{mainfig}. This can be accomplished by developing an SQI map $\boldsymbol f : \boldsymbol x_t \mapsto \boldsymbol y_t$ such that
\begin{equation} \label{goal1}
	\boldsymbol f(\boldsymbol x_t) = \boldsymbol y_t  = \left\{ \begin{array}{cl} 1 & \mbox{if } ~ \boldsymbol x_t^{\mbox{\scriptsize motion}} \approx 0 \\ 0 & \mbox{if } ~ \boldsymbol x_t^{\mbox{\scriptsize motion}} \not\approx 0 \\ \end{array}\right.
\end{equation}
However, it is arduous to achieve \eqref{goal1}, where the assessment is conducted on an individual CVS at every sampling time. Instead, we take advantage of the periodicity and regularity of cardiac volume changes according to the heartbeat. The time interval of each cardiac cycle is identified using a synchronized ECG system.

Our quality assessment is conducted on every cardiac cycle of CVS, where a cardiac cycle is defined by the time interval consisting of two consecutive ECG R-wave peaks as the end points. For a given time $t_{\mbox{\scriptsize cyc}}$, let the interval $[t_{\mbox{\scriptsize cyc}},t_{\mbox{\scriptsize cyc}}+\Delta t_{\mbox{\scriptsize cyc}}]$ be the corresponding cardiac cycle, where $\Delta t_{\mbox{\scriptsize cyc}}$ is assumed to be $\Delta t_{\mbox{\scriptsize cycle}} = 10\mbox{ms} \times (\boldsymbol v-1)$ for some $\boldsymbol v \in \mathbb{N} \backslash \{1\}$. Here, $\mathbb{N}$ denotes the set of positive integers. A vector gathering all CVSs during the cycle, denoted by $\boldsymbol X_{t_{\mbox{\scriptsize cyc}}} \in \mathbb{R}^{\boldsymbol v}$, is defined as
\begin{equation}\label{CVSvec}
	\boldsymbol X_{t_{\mbox{\scriptsize cyc}}} = \begin{bmatrix} \boldsymbol x_{t_{\mbox{\scriptsize cyc}}}, \boldsymbol x_{t_{\mbox{\scriptsize cyc}}+10\mbox{\scriptsize ms}}, \cdots, \boldsymbol x_{t_{\mbox{\scriptsize cyc}}+10\mbox{\scriptsize ms} \times (\boldsymbol v-1)} \end{bmatrix} ^T
\end{equation}
The map $f$ in \eqref{goal1} can be modified into
\begin{equation} \label{goal2}
	\boldsymbol f(\boldsymbol X_{t_{\mbox{\scriptsize cyc}}}) = \boldsymbol y_{t} = \left\{ \begin{array}{cl} 1 & \mbox{for normal } \boldsymbol X_{t_{\mbox{\scriptsize cyc}}} \\ 0 & \mbox{for motion-influenced }\boldsymbol X_{t_{\mbox{\scriptsize cyc}}} \\\end{array}\right.
\end{equation}

To find $\boldsymbol f$ in \eqref{goal2}, we leverage ML, which can learn the domain knowledge of normal and motion-influenced CVSs from a training dataset of $N$ data pairs $\{\boldsymbol X^{(i)}, \boldsymbol y^{(i)}\}_{i=1}^{N}$. Prior to ML applications, the following issues need to be addressed in the CVS data. First, CVSs have significant inter-subject and intra-subject variability. This is because cardiac volume varies depending on various factors, including sex, age, condition, time, and body temperature. Therefore, scale normalization is required to enhance the stability and performance of ML while mitigating the high learning complexity associated with scale-invariant feature extraction \cite{Goodfellow2016,Xu2014}. Second, the dimensions of the input CVS data in \eqref{goal2} do not match each other (i.e., $\boldsymbol v$ is not constant) owing to heart rate variability \cite{Conny1993}. Because most existing ML methods are based on an input with consistent dimensions, size normalization is required. Figure \ref{CVSNormal} schematically illustrates the overall process.

\begin{figure}[h]
	\centering
	\includegraphics[width=1\textwidth]{fig4}
	\caption{From the monitoring system, electrocadiography and cardiac volume signals are obtained. By identifying a cardiac cycle through electrocadiography data (R-wave peak detection), we extract cardiac volume signals at the corresponding cycle and then lastly apply normalization in terms of scale and size.}
	\label{CVSNormal}
\end{figure}

\subsubsection{Scale normalization}
A simple method of normalizing the scale is to rescale the CVS data for individual cardiac cycles. Specifically, for a given CVS vector $\boldsymbol X_{t_{\mbox{\scriptsize cyc}}} \in \mathbb{R}^{\boldsymbol v}$, the scaling factor $\mathcal S$ is obtained using
\begin{equation}\label{simplerescale}
	\mathcal S = \underset{i \in \mathcal V}{\mbox{max}} ~ |\boldsymbol x_{t_{\mbox{\tiny cyc}}+10\times i\mbox{\tiny (ms)}}|
\end{equation}
where the index set $\mathcal V$ is given by $\mathcal V=\{0,1,\cdots, \boldsymbol v - 1\}$. Normalized CVS data, denoted by $\underline{\boldsymbol X}_{t_{\mbox{\tiny cyc}}}$, are obtained by
\begin{equation}\label{ScaleNormalization}
	\underline{\boldsymbol X}_{t_{\mbox{\tiny cyc}}} = \dfrac{\boldsymbol X_{t_{\mbox{\tiny cyc}}}}{\mathcal S}
\end{equation}
However, this scaling may not be appropriate to our application for the following reason. Abnormalities in CVS data include sudden increases or decreases in signal amplitude as well as irregular deformations of the shape profile. The normalization in \eqref{ScaleNormalization} can contribute to ignoring rapid amplitude changes.

This study uses the following subject-specific scale normalization strategy. When the EIT device is used to monitor a certain subject, it is supposed that during the initial 20s calibration process, the device measures the normal CVS data available for scale normalization. Let $\mathcal X^{\mbox{\scriptsize subject}}$ be a set of corresponding CVSs given by
\begin{equation}
	\mathcal X^{\mbox{\scriptsize subject}}=\{ \boldsymbol x_{10\times i\mbox{\tiny (ms)}} :  i = -1999, -1998, \cdots, -1 \}
\end{equation}
Using the set $\mathcal X^{\mbox{\scriptsize subject}}$, a subject-specific scaling factor $\mathcal S^{\mbox{\scriptsize subject}}$ is obtained by
\begin{equation}
	\mathcal S^{\mbox{\scriptsize subject}} = \underset{\boldsymbol x \in \mathcal X^{\mbox{\scriptsize subject}}}{\mbox{max}}|\boldsymbol x|
\end{equation}
This scale factor $\mathcal S^{\mbox{\scriptsize subject}}$ is used for the normalization in \eqref{ScaleNormalization} instead of the naive factor $\mathcal S$ in \eqref{simplerescale}.

\subsubsection{Size normalization}
To make the dimensions of the CVS data consistent, a CVS vector $\underline{\boldsymbol X}_{t_{\mbox{\scriptsize cyc}}}$ is embedded into $\mathbb{R}^{\nu}$ for a fixed constant $\boldsymbol \nu$. In the empirical experiment, the embedding space dimension was to be larger than any dimension of the CVS data in our dataset ($\boldsymbol \nu = 150$).

Two normalization methods are considered. The first approach is to resample $\boldsymbol \nu$ points using linear interpolation with $\boldsymbol v$ data points in $\underline{\boldsymbol X}_t$. For the stationary interval $[0,1]$, the following linear interpolation function $\mathcal L$ is constructed:
\begin{equation}
	\mathcal L(\dfrac{i}{\boldsymbol v-1}) = \underline{\boldsymbol x}_{t_{\mbox{\tiny cyc}}+10\mbox{\tiny (ms)} \times (i-1)}  \mbox{ for } i = 0,\cdots,\boldsymbol v-1
\end{equation}
Subsequently, we obtain the normalized vector $\overline{\boldsymbol X}_{t_{\mbox{\tiny cyc}}} \in \mathbb{R}^{\boldsymbol \nu}$ using
\begin{equation}
	\overline{\boldsymbol X}_{t_{\mbox{\tiny cyc}}} = \left[ ~ \mathcal L(0), \mathcal L(\dfrac{1}{\boldsymbol \nu - 1}), \mathcal L(\dfrac{2}{\boldsymbol \nu - 1}), \cdots, \mathcal L(1) ~ \right]^T
\end{equation}
This method normalizes the signal profile of CVS data into the desired length ($\boldsymbol \nu$) with no significant loss, but loses sampling time information. Second, the last value in $\underline{\boldsymbol X}_{t_{\mbox{\scriptsize cyc}}}$ (i.e., $\boldsymbol x_{t_{\mbox{\scriptsize cyc}}+10\mbox{\scriptsize (ms)} \times (\boldsymbol v-1)}$) is padded up to the desired length. This constant padding provides a vector $\overline{\boldsymbol X}_{t_{\mbox{\scriptsize cyc}}} \in \mathbb{R}^{\boldsymbol \nu}$, expressed by
\begin{align}
	\overline{\boldsymbol X}_{t_{\mbox{\tiny cyc}}} = &[ ~ \underline{\boldsymbol x}_{t_{\mbox{\tiny cyc}}}, \cdots, \underline{\boldsymbol x}_{t_{\mbox{\tiny cyc}}+10\mbox{\tiny (ms)} \times (\boldsymbol v-2)}, \underline{\boldsymbol x}_{t_{\mbox{\tiny cyc}}+10\mbox{\tiny (ms)} \times (\boldsymbol v-1)},  \\ & ~~ \underline{\boldsymbol x}_{t_{\mbox{\tiny cyc}}+10\mbox{\tiny (ms)} \times (\boldsymbol v-1)}, \cdots, \underline{\boldsymbol x}_{t_{\mbox{\tiny cyc}}+10\mbox{\tiny (ms)} \times (\boldsymbol v-1)} ~ ]^T\label{PADD}
\end{align}
where the part \eqref{PADD} corresponds to the padding. In contrast to the first method, this normalization can preserve time information regarding sampling frequency, whereas the core profile of the CVS is supported at different time intervals.

\subsection{Machine Learning Application}
At this point, we are ready to apply ML for determining the SQI function \eqref{goal2}. Collected from various subjects and cardiac cycles, the following dataset is used:
\begin{equation}\label{dataset}
	\{ \overline{\boldsymbol X}^{(i)}, \boldsymbol y^{(i)}\}_{i=1}^{N}
\end{equation}
where $\boldsymbol y^{(i)}$ is the SQI label corresponding to $\overline{\boldsymbol X}^{(i)}$. We note that $\overline{\boldsymbol X}$ is the CVS data for a cardiac cycle of some subjects and is normalized for both scale and size. In practice, the available training dataset \eqref{dataset} was highly imbalanced, where there were relatively few negative samples (motion-influenced CVSs).

\begin{figure}[h]
	\centering
	\includegraphics[width=0.975\textwidth]{fig5}
	\caption{(a) Discriminative-model approach learns a signal quality indexing map $f$ by using CVS and label data. (b) Manifold-learning approach first learns common features of normal CVS data by finding a low dimensional manifold $\mathcal M^{\mbox{\scriptsize normal}}$. Signal quality assessment is based on computing the residual between original CVS data and projected one onto or near the learned manifold.}
	\label{MLFig}
\end{figure}

\subsubsection{Discriminative-model approach}
The discriminative-model approach trains the SQI map $\boldsymbol f : \overline{\boldsymbol X} \mapsto \boldsymbol y$ in the following sense:
\begin{equation}
	\boldsymbol f = \underset{\boldsymbol f \in \mathbb{F}}{\mbox{argmin}} ~ \dfrac{1}{N}\sum_{i=1}^{N}\mbox{dist}(\boldsymbol f(\overline{\boldsymbol X}^{(i)}),\boldsymbol y^{(i)})
\end{equation}
where $\mathbb{F}$ is a set of learnable functions for a given ML model and $\mbox{dist}$ is a metric that measures the difference between the ML output $f(\overline{\boldsymbol X})$ and label $\boldsymbol y$.  See Figure \ref{MLFig} (a). In our application with high class-imbalance, the following weighted cross-entropy can be used:
\begin{equation}
	\mbox{dist}(\boldsymbol f(\overline{\boldsymbol X}),\boldsymbol y) = - \boldsymbol \zeta_{\mbox{\scriptsize pos}}\boldsymbol y\mbox{log}(\boldsymbol f(\overline{\boldsymbol X}))- \boldsymbol \zeta_{\mbox{\scriptsize neg}}(1-\boldsymbol y) \mbox{log}(1-\boldsymbol f(\overline{\boldsymbol X}))
\end{equation}
where $\boldsymbol \zeta_{\mbox{\scriptsize pos}}$ and $\boldsymbol \zeta_{\mbox{\scriptsize neg}}$ are the relative ratios of the positive and negative samples, respectively. Various classification models can be used, such as the logistic regression model (LR) \cite{Cramer2002}, multi-layer perceptron (MLP) \cite{Hinton2007}, and convolutional neural networks (CNN) \cite{Simonyan2014}. Detailed models used in this study are explained in Appendix \ref{appendixB1}.

The discriminative model approach is a powerful method to guarantee high performance in a fixed dataset. However, it might suffer from providing stable SQI results in clinical practice because of highly variable negative samples. This is because these methods take advantage of learned information using only a few negative samples \cite{Buda2018,Cao2019,He2009,Van2017}. To achieve stable prediction, the manifold-learning approach can be alternatively used \cite{Chapelle2009,Van2020,Zhu2009}.

\subsubsection{Manifold-learning approach} \label{sec:semi-sup}
The manifold-learning approach learns common features from positive samples (i.e., normal CVS) and uses them to develop an SQI map. The remaining negative samples are utilized as auxiliary means for selecting a hyperparameter. Figure \ref{MLFig} (b) shows a schematic description of this process.

A set of positive samples is denoted by $\{ \overline{\boldsymbol X}_{\mbox{\scriptsize pos}}^{(i)}\}_{i=1}^{N_{\mbox{\scriptsize pos}}}$, where $N_{\mbox{\scriptsize pos}}$ denotes the number of positive samples. In the first step, we learn a low-dimensional representation of $\overline{\boldsymbol X}_{\mbox{\scriptsize pos}}$ by training an encoder $\mathcal E : \overline{\boldsymbol X}_{\mbox{\scriptsize pos}} \mapsto \boldsymbol z$ and decoder $\mathcal D : \boldsymbol z \mapsto \overline{\boldsymbol X}_{\mbox{\scriptsize pos}}$ in the following sense \cite{Jolliffe2016,Hinton2006}:
\begin{equation}\label{AELoss}
	(\mathcal D, \mathcal E) = \underset{(\mathcal D, \mathcal E)}{\mbox{argmin}} ~ \dfrac{1}{N_{\mbox{\scriptsize pos}}}\sum_{i=1}^{N_{\mbox{\scriptsize pos}}}\| \mathcal D \circ \mathcal E (\overline{\boldsymbol X}_{\mbox{\scriptsize pos}}^{(i)}) - \overline{\boldsymbol X}_{\mbox{\scriptsize pos}}^{(i)} \|_{2}^2
\end{equation}
where $\boldsymbol z$ is a low dimensional latent vector and $\|\cdot\|_2$ is the standard Euclidean norm. The architectures $\mathcal D$ and $\mathcal E$ can be used in PCA \cite{Jolliffe2016}, VAE \cite{Kingma2013}, and $\beta$-VAE \cite{Higgins2016}. See more details in Appendix \ref{appendixB2}.

Borrowing the idea from \cite{An2015}, an SQI map $f$ is constructed as follows: For a given CVS data $\overline{\boldsymbol X}$ in any class, a residual $\boldsymbol r$ is computed by
\begin{equation}\label{residual}
	\boldsymbol r = \| \overline{\boldsymbol X} - \mathcal D \circ \mathcal E(\overline{\boldsymbol X})\|_{2}
\end{equation}
The decoder $\mathcal D$ is trained to generate normal CVS-like output. In other words, operation $\mathcal D \circ \mathcal E$ transforms $\overline{\boldsymbol X}$ to lie in or near the learned manifold using normal CVS data \cite{Seo2013,Yun2022}. Therefore, the residual $\boldsymbol r$ can be viewed as an anomaly score, where $\boldsymbol r$ is small if $\overline{\boldsymbol X}$ is normal CVS data, and large if $\overline{\boldsymbol X}$ is motion-influenced CVS data. For some non-negative constant $d$, an SQI map $\boldsymbol f$ can be constructed using
\begin{equation}\label{selfsupclassi}
	\boldsymbol f(\overline{\boldsymbol X}_t) = \left \{ \begin{array}{cl} 1 & \mbox{if } \boldsymbol r \leq  \boldsymbol d \\ 0 & \mbox{if } \boldsymbol r > \boldsymbol d \end{array} \right.
\end{equation}

The remainder of this subsection explains how the thresholding value $\boldsymbol d$  is determined by utilizing negative samples as well as positive. By varying $\boldsymbol d$ from 0 to $\infty$, a receiver operating characteristic (ROC) curve is calculated, where a point in the ROC curve is obtained using a fixed $\boldsymbol d$. We choose $\boldsymbol d$ such that maximizing Youden's $\boldsymbol J$ statistics, which is known as an unbiased metric in the class imbalance case \cite{Ruopp2008}. The value $\boldsymbol J$ is given by
\begin{equation} \label{Jstatictics}
	\boldsymbol J^{\boldsymbol d}  = \mbox{Sensitivity}^{\boldsymbol d} + \mbox{Specificity}^{\boldsymbol d} - 1
\end{equation}
where
\begin{equation}
	\mbox{Sensitivity}^{\boldsymbol d} = \dfrac{N_{\mbox{\scriptsize TP}}^{\boldsymbol d}}{N_{\mbox{\scriptsize TP}}^{\boldsymbol d}+N_{\mbox{\scriptsize FN}}^{\boldsymbol d}} \mbox{ and }\mbox{Specificity}^{\boldsymbol d} = \dfrac{N_{\mbox{\scriptsize TN}}^{\boldsymbol d}}{N_{\mbox{\scriptsize TN}}^{d}+N_{\mbox{\scriptsize FP}}^{\boldsymbol d}}
\end{equation}
Here, $N_{\mbox{\scriptsize TP}}^{\boldsymbol d}$, $N_{\mbox{\scriptsize TN}}^{\boldsymbol d}$, $N_{\mbox{\scriptsize FP}}^{\boldsymbol d}$, and $N_{\mbox{\scriptsize FN}}^{\boldsymbol d}$ respectively represent the number of true positives, true negatives, false positives, and false negatives for predictions depending on a selected threshold value $\boldsymbol d$.

\section{Results}
\subsection{Data Acquisition and Experimental Setting}
Our dataset was obtained from healthy volunteers using an EIT-based hemodynamic monitoring device (HemoVista, BiLab, South Korea). Synchronized ECG data were obtained with EIT and used to identify the cardiac cycles. While lying in a hospital bed, each subject was requested to make intentional motions mimicking postural changes in the clinical ward. A total of 16140 CVS data were obtained regarding the cardiac cycle.

Manual labeling was individually performed by two- and ten- years bio-signal experts (Nam and Lee). Subsequently, they reviewed the results and made the final decision about CVS abnormality through an agreement between them. The final labels were annotated into three classes: normal, ambiguous, and motion-influenced. When classified as normal or abnormal by both experts with an agreement, CVS data were annotated as normal or motion-influenced classes. The ambiguous class stands for CVS data in which motion artifacts were included with high possibility, but the experts did not reach an explicit agreement about motion influence. The assigned label is $\boldsymbol y = 1$ for the normal class and $\boldsymbol y = 0$ for the other classes. As a result, 12928 (80.09$\%$), 1526 (9.45$\%$), and 1686 (10.45$\%$) samples were labeled as normal, ambiguous, and motion-influenced classes, respectively.

For ML applications, a total of 16372 CVS data were divided into 13100 (80$\%$), 1520 (10$\%$), and 1520 (10$\%$), which were used for training, validation, and testing, respectively. The data split was performed such that CVS data obtained from a common subject did not exist between the three sets. For the training dataset, labels for the ambiguous class were reassigned to $\boldsymbol y = 0.25$. This was done to prevent the over-classification of ambiguous classes.

ML experiments were conducted in a computer system with GeForce RTX 3080 Ti, Intel® Core™ X-series Processors i9-10900X, and 128GB DDR4 RAM. Python with scikit-learn and Pytorch packages were used for the ML implementation. When training the ML models, the Adam optimizer was consistently employed, which is an effective adaptive stochastic gradient descent method \cite{Kingma2014}. Hyperparameters such as epoch and learning rate were heuristically chosen based on the validation results.

\subsection{Results of CVS Quality Assessment}
We compared the performance of the ML-based CVS quality assessment results by using six metrics: accuracy, positive and negative predictive values (PPV and NPV), sensitivity, specificity, and AUC. Accuracy, PPV, and NPV were defined by
\begin{equation}
	\mbox{Accuracy} = \dfrac{N_{\mbox{\scriptsize TP}} + N_{\mbox{\scriptsize TN}}}{N_{\mbox{\scriptsize TP}}+N_{\mbox{\scriptsize TN}}+N_{\mbox{\scriptsize FP}}+N_{\mbox{\scriptsize FN}}}, \mbox{PPV} = \dfrac{N_{\mbox{\scriptsize TP}}}{N_{\mbox{\scriptsize TP}}+N_{\mbox{\scriptsize FP}}}, \mbox{ and } \mbox{NPV} = \dfrac{N_{\mbox{\scriptsize TN}}}{N_{\mbox{\scriptsize TN}}+N_{\mbox{\scriptsize FN}}}
\end{equation}
and AUC was the area under the ROC curve. NPV, specificity, and AUC should be emphasized in our evaluation owing to the high-class imbalance (small negative samples).

\begin{table}[h]
	{\scriptsize
	\begin{center}
		\textbf{(a) SQI with scale and size normalization using linear interpolation.}
		\begin{tabular}{|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|} \hline
			\multicolumn{2}{|c|}{\textbf{Discriminative Model}} & \textbf{LR} & \textbf{MLP1} & \textbf{MLP2} & \textbf{VGG16-3} & \textbf{VGG16-4} & \textbf{VGG16-5} \\ \hline
			\multirow{6}{*}{\textbf{Test}} & \textbf{Accuracy} & 0.8665 & 0.9323 & 0.9348 & 0.9468 & 0.9468 & 0.9437 \\
			& \textbf{PPV} & 1.0000 & 0.9790 & 0.9747 & 0.9525 & 0.9605 & 0.9679 \\
			& \textbf{NPV} & 0.1097 & 0.7241 & 0.7445 & 0.9047 & 0.8591 & 0.8083 \\
			& \textbf{Sensitivity} & 0.8643 & 0.9404 & 0.9479  & 0.9866 & 0.9776 & 0.9657 \\
			& \textbf{Specificity} & 1.0000 & 0.8860 & 0.8607 & 0.7215 & 0.7721 & 0.8185 \\
			& \textbf{AUC} & 0.6615 & 0.9506 & 0.9558 & 0.9709 & 0.9645 & 0.9653 \\ \hline
			\multicolumn{2}{|c|}{\textbf{Manifold-learning Model}} & \textbf{PCA} & \textbf{VAE} & \textbf{$\boldsymbol \beta$-VAE} & \textbf{CVAE} & \textbf{$\boldsymbol \beta$-CVAE} & - \\ \hline
			\multirow{6}{*}{\textbf{Test}} & \textbf{Accuracy} & 0.8468 & 0.9066 & 0.9221 & 0.9292 & 0.9298  &\\
			& \textbf{PPV} & 0.9510 & 0.9687 & 0.9672 & 0.9688 & 0.9739 & \\
			& \textbf{NPV} & 0.4573  & 0.6181 & 0.6900 & 0.7100 & 0.7011 & \\
			& \textbf{Sensitivity} & 0.8675 & 0.9218 & 0.9439 & 0.9486 & 0.9441 & -\\
			& \textbf{Specificity} & 0.7142 & 0.8095 & 0.7952 & 0.8047 & 0.8380 & \\
			& \textbf{AUC} & 0.8735 & 0.9513 & 0.9489 & 0.9528 & 0.9603 & \\ \hline
		\end{tabular}
	\end{center}
	\begin{center}
		\textbf{(b) SQI with scale and size normalization using constant padding.}
		\begin{tabular}{|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|} \hline
			\multicolumn{2}{|c|}{\textbf{Discriminative Model}} & \textbf{LR} & \textbf{MLP1} & \textbf{MLP2} & \textbf{VGG16-3} & \textbf{VGG16-4} & \textbf{VGG16-5} \\ \hline
			\multirow{6}{*}{\textbf{Test}} & \textbf{Accuracy} & 0.8664 & 0.9487 & 0.9518 & 0.9455 & 0.9487 & 0.9500 \\
			& \textbf{PPV} & 1.0000 & 0.9745 & 0.9767 & 0.9533 & 0.9655 & 0.9731 \\
			& \textbf{NPV} & 0.0826 & 0.7851 & 0.8065 & 0.8870 & 0.8433 & 0.8185 \\
			& \textbf{Sensitivity} & 0.8648 & 0.9651 & 0.9666 & 0.9844 & 0.9748 & 0.9681 \\
			& \textbf{Specificity} & 1.0000 & 0.8521 & 0.8652 & 0.7173 & 0.7956 & 0.8434 \\
			& \textbf{AUC} & 0.6628 & 0.9725 & 0.9669 & 0.9782 & 0.9683 & 0.9757 \\ \hline
			\multicolumn{2}{|c|}{\textbf{Manifold-learning Model}} & \textbf{PCA} & \textbf{VAE} & \textbf{$\boldsymbol \beta$-VAE} & \textbf{CVAE} & \textbf{$\boldsymbol \beta$-CVAE} & - \\ \hline
			\multirow{6}{*}{\textbf{Test}} & \textbf{Accuracy} & 0.8809 & 0.8918 & 0.9214 & 0.9015 & 0.8861 &\\
			& \textbf{PPV} & 0.9590 & 0.9660 & 0.9679 & 0.9731 & 0.9636 & \\
			& \textbf{NPV} & 0.5333 & 0.5629 & 0.6694 & 0.5882 & 0.5467 & \\
			& \textbf{Sensitivity} & 0.9014 & 0.9074 & 0.9407 & 0.9118 & 0.9029 & -\\
			& \textbf{Specificity} & 0.7450 & 0.7892 & 0.7941 & 0.8333 & 0.7745 & \\
			& \textbf{AUC} & 0.9150 & 0.9206 & 0.9412 & 0.9170 & 0.9041 & \\ \hline
		\end{tabular}
	\end{center}}
	\caption{Machine learning-based CVS quality assessment results}	\label{result}
\end{table}

\subsubsection{Discriminative Models}
The first and second rows of Tables \ref{result} (a) and (b) show the quantitative evaluations of CVS quality assessment using various discriminative models: LR, MLPs, and CNNs. The results in Tables \ref{result} (a) and (b) differ in size normalization: (a) linear interpolation and (b) constant padding.

MLPs and CNNs performed better than LR, which provided miserable NPV and AUC. MLPs and CNNs outperformed each other in specificity and NVP respectively, while achieving comparable levels for the other metrics. There was no significant performance gap depending on the size normalization.

One interesting observation was as follows: In our experiments, there seems to be a compensation between specificity and NPV, depending on the emphasis on locality and globality. Enriching global information on CVS data positively affected specificity; in contrast, local information helped improve NPV. As the receptive field size in VGG16 increased (see Appendix \ref{appendixB1}), specificity tended to increase and NPV decrease. In MLP, which is more flexible for catching global information than CNNs, specificity was highest, and NPV lowest. In other words, the local information of CVS data is likely to play a crucial role in reducing false negatives rather than false positives. From a practical point of view, reducing false negatives is more desirable; therefore, using VGG16-3 or VGG16-4, which have the powerful ability to take advantage of locality, can be an excellent option.

\subsubsection{Manifold-learning Models}
\begin{figure}[t]
	\centering
	\includegraphics[width=1\textwidth]{fig6}
	\caption{Test samples and VAE-based projection results for (a) normal and (b) motion-influenced CVS data, where the red line is original CVS data and the blue line is the correspondent CVS data projected by VAE. By the way, ROC curves for (c) VGG 16-4 and (d) $\beta$-VAE are provided, where the blue and red lines correspond to the curves calculated using training and test sets, respectively.}
	\label{Result2}
\end{figure}
Positive samples in the validation set were used for hyperparameter selection in training the encoder and decoder. A threshold value was determined by using data from all the training and validation sets.

Figure \ref{Result2} shows manifold projection results of test samples in normal and motion-influenced classes. An input CVS is projected onto or near a manifold learned by positive samples. As desired, the residual \eqref{residual} tends to be small for normal samples and high for motion-influenced samples.

The third and fourth rows of Tables \ref{result} (a) and (b) show the final assessment results using manifold-learning models. The performance was comparable to that of discriminative models. We note that the manifold-learning models never learned negative samples for classifier development. As shown in Figure \ref{Result2} (d), the manifold-learning model's performance gap between training and test sets was very small.

There was a slight difference in performance for the manifold-learning models depending on the size normalization. Linear interpolation promised a slightly better assessment of accuracy, NPV, and AUC than the other. For the case of constant padding, because core profiles of CVS data are supported at different intervals, the learning complexity can be increased, which is associated with invariant feature extraction to the intervals. This may cause a slight drop in performance.

In our dataset, both discriminative and manifold learning models provided accurate detection of motion-influenced CVS. The discriminative model yielded a more powerful SQI performance; in contrast, the manifold-learning model provided stable outcomes between the training and test sets. Regarding practical applications, the choice of two models relies on what should be emphasized in the monitoring system in terms of performance and stability. Their ensemble is also worth considering.

\subsubsection{Impact of Scale Normalization}
\begin{table}[h]
	\centering
	{\footnotesize
		\begin{tabular}{|p{2cm}|p{1.75cm}p{1.4cm}|p{1.75cm}p{1.4cm}|} \hline
			& \multicolumn{2}{l|}{\textbf{With Scaling}} & \multicolumn{2}{l|}{\textbf{Without Scaling}} \\ \hline
			\textbf{Model} & \textbf{VGG16-3} & \textbf{VAE} & \textbf{VGG16-3} & \textbf{VAE} \\ \hline
			\textbf{Accuracy} & 0.9468 & 0.9066 & 0.7862 & 0.7509 \\
			\textbf{PPV} & 0.9525 & 0.9687 & 0.9763 & 0.9668 \\
			\textbf{NPV} & 0.9047 & 0.6181 & 0.4038 & 0.3327 \\
			\textbf{Sensitivity} & 0.9886 & 0.9218 & 0.7671 & 0.7373 \\
			\textbf{Specificity} & 0.7215 & 0.8095 & 0.8945 & 0.8380 \\
			\textbf{AUC} & 0.9709 & 0.9513 & 0.9067 & 0.8906 \\ \hline
	\end{tabular}}
	\caption{Results of machine learning-based CVS quality assessment with and without scale normalization.}
	\label{ScaleNormal}
\end{table}

Table \ref{ScaleNormal} shows the worst case when scale normalization was not applied. In CNNs, network training was very unstable, and assessment performance was considerably degraded, especially regarding accuracy, NPV, sensitivity, and AUC. In VAEs, large-scale variability of CVS data highly affected the loss of accuracy in manifold projection; therefore, the performance significantly deteriorated in terms of accuracy, NPV, sensitivity, and AUC. This verifies the impact of scale normalization.

\subsubsection{Inference Time}
\begin{table}[h]
	\centering
	{\footnotesize
		\begin{tabular}{|p{1.75cm}|p{1.75cm}|p{1.75cm}|p{1.75cm}|p{1.75cm}|p{1.75cm}|} \hline
			\textbf{Model} & \textbf{LR} & \textbf{MLP1} & \textbf{MLP2} & \textbf{VGG16-3} & \textbf{VGG16-4}  \\ \hline
			\textbf{Time} & 0.633$\mu$s & 1.265$\mu$s & 0.700$\mu$s & 1.897$\mu$s & 3.162$\mu$s \\ \hline
			\textbf{Model} & \textbf{VGG16-5} & \textbf{PCA} & \textbf{VAE} &\textbf{CVAE} & - \\ \hline
			\textbf{Time} & 3.562$\mu$s & 48.412$\mu$s & 3.703$\mu$s & 15.192$\mu$s & -  \\ \hline
	\end{tabular}}
	\caption{Test inference time of machine learning-based CVS quality assessment methods.}
	\label{InferenceTime}
\end{table}

In real-time monitoring, assessment should be performed quickly. The input for the proposed method was updated for every heartbeat in the EIT system. Assuming a subject with a constant 80bpm, the CVS input is updated every 0.75s. Roughly, the assessment should be faster than approximately $10^{-2}$s. Table \ref{InferenceTime} shows the inference time for the test data, calculated by taking the average over the entire test data. The ML models provided a test outcome with inference times between 100$\mu$s ($10^{-4}$s) and 0.1$\mu$s ($10^{-7}$s). This confirms that the proposed method meets the speed requirements for real-time monitoring.

\section{Conclusion and Discussion}
We developed a novel automated SQI method using two machine learning techniques, the discriminative model and manifold learning, to detect abnormal CVS caused by motion-induced artifacts. We discussed how body movement influences the transconductance data and how the resulting CVS is degraded by movement. Numerous experiments support the idea that the proposed method can successfuly filter motion-induced unrealistic variations in CVS data.

To the best of our knowledge, this is the first attempt to assess CVS quality to enhance the clinical capability of an EIT-based cardiopulmonary monitoring system. From a practical point of view, the proposed method can alert clinicians about CVS corruption to minimize misinformation about patient safety and facilitate adequate management of patients and medical resources. The proposed method can be combined with a software system for existing EIT devices.

The use of only healthy subject data in the training process did not fully consider possible influence of the subject's illness on CVS. SQI performance might be degraded in patients with illnesses such as arrhythmias, in which irregular deformation may occur in CVS due to premature ventricular contraction and lead to be classified as low signal quality. However, when ill patient data are available and appended in the training process, a slightly modified SQI can detect the illness and motion by adding another label class. Meanwhile, arrhythmia can be easily detected using ECG signals.

A further collection of CVS data could be a strategy for enhancing model generalization or stability toward being equipped with an actual monitoring system. In discriminative models, even with additional data collection, generalization or stability might not be meaningfully improved because the class imbalance problem remains or increases. In contrast, the manifold-learning models can accurately infer common features (i.e., data manifolds) as the total number of normal CVS data grows regardless of class imbalance. In addition, it can be extended into a semi-supervised or unsupervised learning framework \cite{An2015,Schlegl2017}, which reduces the requirement for labeled datasets. Thus, manifold-learning models might be favorable.

\section*{Data Availability}
The data that support the findings of this study are available from the corresponding author, K. Lee, upon reasonable request.

\section*{Acknowledgements}
This work was supported by the Ministry of Trade, Industry and Energy (MOTIE) in Korea through the Industrial Strategic Technology Development Program under Grant 20006024. Hyun was supported by Samsung Science \& Technology Foundation (No. SRFC-IT1902-09). We are deeply grateful to BiLab (Pangyo, South Korea) for their help and collaboration.

\section*{Conflict of Interest}
The authors have no conflicts to disclose. \\

redpajama_set_name: RedPajamaArXiv


text:
Veterans utilize their expertise – and Canon cameras & printers – as part of photography course for fellow former service members
Canon cameras used to help veterans improve their photography skills as part of specially designed courses at Santa Barbara City College
MELVILLE, NY, November 9, 2022 - The branches of the United States military stress the importance of duty and assisting fellow service members. For many veterans, that sentiment extends after being discharged from active duty. For brothers Bill and Steven Espinosa – Vietnam War veterans who served in the Army and Navy, respectively – that desire to help has led them to teach a photography course for fellow veterans in a class offered through Santa Barbara City College in California.
The introductory course – "Digital Photography: Techniques and Creative Applications in a Community-Oriented Setting" – gives students access to digital single-lens reflex cameras (DSLRs), with many using this type of camera for the first time. Canon has provided multiple EOS Rebel T6 cameras for the class and is in the process of loaning other equipment in continuing to offer resources to take the best photos possible while also being able to produce their work on Canon imagePROGRAF photographic printers.
Both Bill and Steven began as students in the class, which was started by Terence Ford and his fStop Foundation, whose goal was to offer digital photography instruction to veterans and first responders at Santa Barbara City College's Wake Campus. Ford, the brother of actor Harrison Ford, had worked in commercial photography and the film industry in Los Angeles for decades before starting the photography class.
"It's important to provide an opportunity for veterans to reconnect and both offer and receive peer support to develop a community where everyone feels engaged," said Ford said, whose program was also embedded at Camp Pendleton's Wounded Warrior Battalion West and four years at the Pathway Home at the Soldiers Home in Yountville, California. "Programs such as these photography classes allows veterans to learn new skills while forging social connections, creating a strong community."
The class proved popular and soon the brothers became part of the Veteran Photography Program. That initiative has made an impact on the lives of former service members, both in the class and in the larger community.
Some of the students in the course have selected their best work from the semester, and printed and framed the photos to decorate Johnson Court, an affordable housing development that serves the Santa Barbara homeless veteran population. The photographs were printed via Canon Professional large format printers , including the 24-inch imagePROGRAF PRO 2100 and the 17-inch Canon imagePROGRAF PRO-1000 printers. In total, 20 photos taken on Canon cameras were selected, professionally printed and framed. The veterans living in Johnson Court then selected which ones they wanted to decorate their unit.
Steven Espinosa, who worked 31 years as a firefighter for the Santa Barbara City Fire Department, and Bill Espinosa helped raise more than $3,500 towards the endeavor.
"It wouldn't be possible if we didn't have Canon support and we're really appreciative of their efforts," said Bill Espinosa, who used the GI bill to help earn a MBA degree that launched a varied career in planning "We're so thankful for Santa Barbara City College having this program and for all the organizations who were kind enough to contribute."
Steven Espinosa said the response was positive and he is hopeful that the success of the program could lead to expansion, and that the photos decorating units at Johnson Court will inspire some of the veterans living there to sign up for a future course.
"So far, we've kept the classes small, and this semester it includes 12 students," he said. "There is a lot of one-on-one work and if we had a big class or we had to do it remotely, it wouldn't work as well. But we do eventually want to extend it to first responders and feel that would be a great step forward."
Santa Barbara City College is also a Canon Solutions America customer within the enterprise group.
Santa Barbara City College's School of Extended Learning is looking to expand the program by inviting first responders or retired first responders to enroll in the free course beginning in January 2023. Registration will open on December 15. To register please email either Bill Espinosa - wespinosa@pipeline.sbcc.edu or Steve Espinosa - spespinosa@pipeline.sbcc.edu.
Canon has provided multiple cameras and printers for the veterans photography course

redpajama_set_name: RedPajamaCommonCrawl


text:
Category Archives: Very Pure & Ambiguous – The Prequel
Very Pure & Ambiguous - The Prequel
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VPnA – The Prequel Volume 2 Chapter 170
June 28, 2019 Re:Library	3 Comments
Final chapter~
So here's what I'm going to be doing in regards to the novel:
I'm going to take a break before starting to 'write the fanfic', please don't pester me on Discord about how long the break is going to be, because the only thing I can tell you is that I want start posting the 'fanfic' in late January/early February (when I finish my internship) with a bang, then proceed to finish the novel ASAP. For my patrons on Patreon, don't fret too much, I'll try to update you guys on the progress at least once a week, and will occasionally throw out chapter previews on Patreon.
Now, 'writing fanfic' isn't going to be the best solution to the problem, but it really is the only thing I can do without dropping the novel (well, technically I could keep translating until I get hit by a DMCA or a threat or something, but I don't want to have to put up with that). I love the novel as much as any of you, and it being the first CN novel I translated really makes me emotionally attached to it.
Penultimate chapter~
Chapter 170 – I'll Only Give You Five Hundred
Next ⇒
On the next day, the three of us arrived at Lu Baoqiang punctually. The person that went with us was an expert from a reputed appraisal center.
"Mr. Lu, this is Mr. Yang, an expert from Yanjing city's XX Antique Store," I said to Lu Baoqiang.
"Hello, Mr. Yang!" Lu Baoqiang was someone belonging to the upper class, so he naturally heard of Mr. Yang's name previously.
"Okay, Mr. Lu, can you take out the fragments for Mr. Yang to appraise it?" I asked.
"Sure, sure! Naturally!" Lu Baoqiang said calmly, showing a completely different expression from the vigilance one the previous day.
Lu Baoqiang took the box out and placed it in front of us. Mr. Yang took out his magnifying glass, and started to carefully examine the fragments.
Meanwhile, Lu Baoqiang acted naturally as if he was completely confident.
On the other hand, Xu Er's face displayed his nervousness and unease.
"Mr. Yang, how is it?" Lu Baoqiang asked with a smile.
"It's real! This is a vase used by officials during the Qing Dynasty!" Mr. Yang put down his magnifying glass and said confidently.
"Mr. Yang, are you sure?" I intentionally asked.
"Yes, I'm sure!" Mr. Yang nodded.
"Haha! How is it? I didn't lie to you guys, right? I told you that there's no need for the appraisal!" Lu Baoqiang smiled, while his heart bled! That was a vase worth a fortune! But, it is worth it if he could get a flower-like beauty in return!
"Oh, since it's like this, we should fulfill our end of the promise…" I received the box with the fragments and took a look before turning to Lu Baoqiang. "However, I coincidentally received a video, I wonder if Mr. Yang is interested?"
"Video? What video? What do you mean?" Lu Baoqiang asked in confusion.
"Hehe, this video is naturally related to you, Mr. Lu. Of course, it is also related to this vase! How is it? Mr. Lu, don't you want to have a look!?" I asked with a smile that wasn't completely like a smile.
"Heh! What kind of tricks do you want to play? Mr. Yang aready appraised the fragments of the vase, what use is a video?" Lu Baoqiang said in dissatisfaction.
"This video naturally has its use, it might even be the key! Du Xiaowei, bring the notebook over!" I said to Du Xiaowei.
"Okay!" Du Xiaowei answered, then took out a laptop from his backpack and placed it onto the tea table.
"Just what are you guys doing! I'm telling you, my time is very valuable, I have an important meeting to get to later! A single minute is worth several thousand yuan, can you afford to pay that much!" Lu Baoqiang said unhappily.
"Don't be so anxious. Mr. Lu, you'll understand after we watch it together, why be in such a hurry?" I said.
"Heh! Since Xu Xueyun is already mine, what use is you doing all this!" Lu Baoqiang snorted.
"Perhaps not, right?" I pressed play and said coldly to Lu Baoqiang.
"What did you say…!?" Lu Baoqiang was completely stunned before he finished!
That was because the person on the screen was him! And the background was this very living room!
In the video, Lu Baoqiang picked up the vase and smashed it onto the floor!
At that moment, Lu Baoqiang's expression had already turned very dark. He couldn't believe that what he did actually appeared on the laptop screen in front of him.
"Impossible!" Lu Baoqiang exclaimed. "How did you guys get that? Oh… I know, you guys placed a surveillance camera in my room!"
"Mr. Lu, don't care about how we got the video for now. Hehe, it seems like you smashed your own vase?" I asked calmly.
"So-So what if I smashed the vase? I just like to smash vases for fun when I have nothing to do. It's my own vase, I can smash it however I like. So? I like it!" Lu Baoqiang roared out his defense. "What's more, what proof do you have for saying that the vase I smashed is the fragments you see?"
"Hehe, I knew you would say that. Then, please have a look at the following photos. I got Xiaowei to take photos of the fragments yesterday!" I clicked open a photo album.
"What does this prove!" Lu Baoqiang could feel that something wasn't quite right.
"Look carefully! The sizes of the fragments in the photo is different from the fragments you showed us today! For example, look at the photo of the fragments of the vase bottom. The crack is in between the characters, but it isn't cracked there at all with the fragments you brought out today!" I replied.
"You-You guys plotted against me!" Lu Baoqiang stood up and shouted angrily.
"We plotted against you? Are you serious? You smashed the vase yourself and you're saying we plotted against you?" I laughed coldly. "Say, Lu Baoqiang, how do you want to handle it? Do you want to sue us, or do you want to just let it go?"
"You…" Lu Baoqiang stuttered in anger. However, the other side had proof on hand, so he wouldn't win in a lawsuit at all. He looked furiously at the laptop screen, then sat down on the sofa weakly. After a while, he finally spoke up. "****, I give up! Brat, I remember you now! You better be careful from now on!"
"Hehe, I've always been careful," I said. "Then we're off. Oh yeah, I forgot to tell you, although your vase shattered, Mr. Yang's antique store is willing to buy it too. You can talk with him about it."
"Fragments can be sold too? How much is it worth?" Hearing that, Lu Baoqiang immediately asked Mr. Yang. At that moment, he was regretting everything way too much! Why was I such an idiot? Now only did I smash the real vase, I didn't even get anything. He merely wanted to see how much the fragments can be sold for to hurry up and minimize his losses.
Mr. Yang nodded.
"Then have a look to decide?" Lu Baoqiang asked impatiently.
Mr. Yang thought about it and reached out five fingers.
"Fifty thousand? So little?" Lu Baoqiang said in disappointment.
Mr. Yang shook his head.
"Five thousand? ****, it can't be! I spent several thousand yuan just to get someone to get the fake vase made!" Ast that moment, Lu Baoqiang no longer cared as he spoke out the truth of the incident.
"Five hundred!" Mr. Yang said. "That's it!"
"What? Five hundred!?" Lu Baoqiang nearly fell down onto the floor! A vase worth tens of millions instantly turned into something that was only worth five hundred! However, just who was to blame?
When we walked out of Lu Baoqiang's home, Xu Er laughed like a crazy person the moment he thought of Lu Baoqiang's look when he realized that he was screwed. Xu Er raised his thumb, "This method was too smart! However, he won't take revenge on my little sister, right?"
"Just tell your little sister not to work here anymore!" I said. "You can support your little sister's school fees already, can't you?"
"Hehe, that's true! But my little sister is very stubborn, she always wants to support herself!" said Xu Er.
"Oh yeah, the name Xu Xueyun sounds so familiar. Where does your sister study?" I asked.
"Huaxia University!" Xu Er said proudly. After all, Huaxia University was one of the best universities within our country!
Continue reading Chapter 170 – I'll Only Give You Five Hundred →
Chapter 169 – Smash Another One
"Hehe, this si a treasure!" Seeing that I didn't speak, Lu Baoqiang added on the side. "Back then, I wanted to spend five hundred thousand to get this chick Xu Xueyun, but she refused. It's great now, this happened. This is all destined!"
Xu Xueyun? Why does this name sound so familiar? I seem to have heard it somewhere. It was only at that point that I found out Xu Er's little sister was called Xu Xueyun. Previously, Xu Er had only been referring to her as his little sister, and I didn't ask what she was called.
Hearing Lu Baoqiang's words, I suddenly had an idea. Heh, I was originally just planning to expose his ploy, but at that moment, I decided to make him lose the bait along with the fish!
I pretended like I didn't understand anything, "Oh, it seems to have Qing Dynasty or something written on it. It truly is something from ancient times!" Then, I casually handed the box to Du Xiaowei.
Hearing my words, Xu Er's expression instantly turned for the wrose.
"Yeah, are you guys going to compensate me with money or make Xu Xueyun be my mistress? If I get happy, I might give her several hundred thousand!" Lu Baoqiang said shamelessly.
I ignored it and smiled, "Since it's like that, we definitely won't; be able to compensate!"
Hearing my words, Lu Baoqiang revealed a happy expression, "Then you guys are agreeing to my terms?"
"Yes, but we can't just accept it like this! You said that this vase is worth five hundred thousand, but there is no proof, so we don't really believe it!" I immediately changed my tone.
"Then what do you want?" Lu Baoqiang's mind was full of Xu Xueyun's great body as he asked in a hurry.
"How about this? We'll take the fragments away and get a professional to appraise it. If it really is worth five hundred thousand, then we'll go as you say. If it's not, then wouldn't we be at a loss?" I said.
"Appraise for what? Just ask around, a vase used by government officials in the Qing Dynasty is definitely worth this much! Five hundred thousand is on the low side, a vase from the Qing Dynasty can be worth several hundred thousand!" Lu Baoqiang replied.
"That's no good, we don't understand anything, we aren't comfortable with it unless we get it appraise!" I said. Actually, I naturally knew that Lu Baoqiang was right! If this was truly something government officials used then, five hundred thousand was truly low. If it was auctioned, then its possible for it to be sold for several million! Or even several tens of millions! However, this vase was fake.
"Ugh…" Lu Baoqiang did not expect that I would say that! Just moments earlier, he was getting beyond happy for getting Xu XUeyun, and now it seemed like things were not going to go that successfully! For him, it seemed like the person in front of him was very troublesome! However, he wasn't willing to just let Xu Xueyun go like that. The moment he thought of her, his sinful lower body started to react. He forcefully calmed himself down and thought about it. "It's like this, I already promised to sell this vase to a business partner. I told him that the vase broke yesterday, but he didn't believe me. He thought that it was just a random excuse I found to not sell it to him, and is coming here tonight to see for himself! How about this, come and get the fragments for appraisal tomorrow!"
**** yeah, this was what I was waiting for! Hehe, how could I not agree?
"Sure, then we'll come over tomorrow! Since its like that, then we'll take our leave!" I looked at Du Xiaowei.
Du Xiaowei returned my look with a signal that said that everything was done, while he returned the fragments to Lu Baoqiang.
The moment we walked out of the door, Xu Er pulled my hand and asked me anxiously, "Director Liu, what did you mean by what you said just now… You mean that my little sister has to be his mistress? Is Director Ding unwilling to lend me the money?"
As I looked at Xu Er, who fired questions at me like a machinegun, I patted his shoulders and smiled, "Don't worry! That isn't the case at all! I'm going to make that Lu Baoqiang lose the fish along with the bait!"
"Lose the fish along with the bait?" Xu Er asked in confusion.
"Yeah! From his tone and expression, Lu Baoqiang is already crazy obsessed with your little sister! Otherwise, he wouldn't have planned and caused his vase incident! Since Lu Baoqiang is willing to pay five hundred thousand to get your little sister, that means he is determined to get her," I continued. "We can use this and screw him over!"
"Use it? Use it how? How are we supposed to screw him over?" Xu Er asked in confusion.
"Hehe, it's actually like this. I got Du Xiaowei too… Like this…" I explained.
"What? Is that going to work? Would Lu Baoqiang do something so crazy?" Xu Er asked in disbelief.
"Hehe, definitely! His lust is probably overcoming his reason now!" I smiled.
At the same time, in Lu Baoqiang's home.
Lu Baoqiang paced around the room hesitantly. He did not expect Xu Er to find a person like that. It seems like everything was going to be a bit difficult! Although it was not going to go as smoothly as he had hoped, it wasn't an impossible matter.
Lu Baoqiang walked to his shelf and opened the glass door to take out a box from the very bottom level. He opened the box's lock with a key. Within the key laid a clay vase. As he looked at it, he felt a bit of reluctance and hesitance.
However, in the very end, Lu Baoqiang still took out the vase, clench his teeth, and raised it over his head. He felt like he had already started to sweat, while his hands also trembled! This vase was something truly from the Qing Dynasty. It was worth far more than five hundred thousand. Back then, he had bought it for three million, and now it was worth more than ten million!
Even though ten million wasn't a lot for Lu Baoqiang, it wasn't a small amount either. It was the annual income for his company. At that very moment, Xu Xueyun's sexy body surfaced in Lu Baoqiang's mind again. That sexy *** and the voluptuous body…
Every single time Xu Xueyun came over to do housework, Lu Baoqiang would always stand behind her to fantasize as he looks at her ***. Every single time she leaves, Lu Baoqiang would rush to the bathroom to deal with his issue. It wasn't that Lu Baoqiang had never seen any women before, but no matter how pretty those women were, they were merely low quality prostitutes without any taste. It wasn't easy to find a beautiful and pure university like Xu Xueyun at all. Lu Baoqiang was able to find a bit of satisfaction every single time he fantasized about Xu Xueyun, so he truly wanted to gain Xu Xueyun for real!
Thinking that, Lu Baoqiang finally decided. He let his hands loose and allowed the vase to flow down onto the flow in a "pa" sound, instantly shattering into smithereens.
As he looked at the floor full of fragments, Lu Baoqiang smiled in a truly lecherous manner…
Lu Baoqiang poured the original fragments into the rubbish bin, then placed the fragments on the floor into the box and returned it to the book shelf.
After doing all that, Lu Baoqiang sat back onto the sofa in satisfaction, closed his eyes and started to fantasize…
Continue reading Chapter 169 – Smash Another One →
Click here for 5/5 chapters.
Alright, I finally got around to posting these chapters. I'm very sorry for posting it so late, but between moving back to HK for internship, getting used to the internship and work, as well as family issues, Very Pure and Ambiguous – The Prequel was more or less the last thing on my mind (considering that I am dropping the novel). You will be getting 2 more chapters from me most likely on Sunday (I promise I'll get it out on Sunday by the latest), but that's going to be it. It's been a good run, but I really don't feel like worrying about my translations getting DMCA'd.
Once again, thank you all for reading this novel with me so far. I sincerely hope that you will stay around to read the Very Pure and Ambiguous – The Prequel fanfiction that I will be writing from now on (but more on that for our truly final release).
June 28, 2019 Re:Library	Leave a comment
June 28, 2019 Re:Library	10 Comments
Chapter 168 – Vase Incident
Du Xiaowei intentionally slowed down. When we passed by the upstart, he deliberately coughed a few times, causing that upstart to clench his teeth in anger and cursed, "**** you!"
"Who are you cursing?" The traffic police immediately got mad when he heard the upstart curse. He has dealt with so many drivers that violated regulations, every single one of them smiled guiltily. This guy actually dared to curse! Just moments ago, the guy was speaking softly, and the moment he turned around, the guy completely changed attitude. Although the person cursed softly, he still heard everything clearly. He had wanted to let the person go once, but now, he immediately changed his mind and decided to punish him with the heaviest punishment.
"I…" The upstart wanted to explain, but noticed that the Santana 2000 was already long gone.
"Take out your driving license!" The traffic police said angrily.
I sent Xia Jing back to my mansion first. Zhao Yanyan and Xia Jing were old friends, while the other girls knew about the purpose of my trip to the US earlier, so they were extra-intimate towards Xia Jing.
I got Xia Jing to stay in the mansion for a few days, then said that I would visit her parents with her.
Then, Xu Er and I went to the study, while he told me about his sister's situation.
Xu Er's little sister used money very carefully while studying in Yanjing city, and she acted as a hourly maid at a rich person's home. The male employer often makes advances on her when the female employer wasn't home, but after she refused, the guy did become more honest. However, he still continued to harass her verbally, and after raises some unacceptable requests, including wanting to take Xu Er's little sister as a mistress.
Although Xu Er's little sister was disgusted by his actions, due to the high wage, she merely endured it and continued to work. However, a recent accident changed all this.
While Xu Er's little sister was wiping the cabinet, she accidentally pushed one of the vase onto the floor. Actually, after the incident, when Xu Er's little sister recalled the situation, she noticed that the vase was put in a very illogical position, and it would be pushed down if she was just slightly careless. However, since it had already happened, thinking about that was no use. Thus, Xu Er's little sister wanted to compensate him with money. Yet, the guy pointed at a small stamp at the bottom of the vase and said that the vase was from a government official in the Ming Dynasty, making it worth a fortune. It was at least worth five hundred thousand!
Xu Er's little sister immediately freaked out after hearing that. Since her birth, the most money she had seen was several thousand yuan, and that was the school fees she brought with her. Five hundred thousand was an astronomical number for her, a number that she would never be able to earn in her entire life.
At that moment, the guy changed his tone and said that as long as she follows him and be his mistress for ten years, that would payout the five hundred thousand. That caused Xu Er's little sister to cry in fear, so she ran out to call Xu Er. Then, after Xu Er arrived in Yanjing city to talk with the male employer, they were unable to get to any sort of result after a long time. The male employer just stubbornly said that the vase was worth five hundred thousand, and the only two solutions were either for Xu Er's little sister to be his mistress or for them to compensate him with money.
The male employer was a local and the director of a company, so he had both money and influence. There was nothing Xu Er could do at all. Xu Er was foreign to Yanjing city and didn't know anyone here. Thinking that they might not even win if they go to court, he could only call me in his helplessness. He didn't dare to tell Ding Baosan directly, and since he knew that I was on good terms with Ding Baosan, he wanted me to help him ask to see if it was possible to get his salary beforehand. That was how the situation where he called me occurred.
"Did you managed to get hold of the vase fragment?" I asked a rather important question. This sort of situation was common later on. Many people placed some fake antiques at home and intentionally got their maids or babysitters to break them in order to extort money from them. Actually, those antiques were just forgeries. Although I wasn't sure that the guy also used a forgery, but just because he seemed to have planned it, he definitely did not have any good intentions, so whether the vase was real or not became of utmost importance.
"No, the person didn't give it to me," said Xu Er.
"You should have asked for the fragment, then got it appraised," I said.
"Appraised? Why… Oh! I understand, you're saying that the vase might be forged?" Xu Er instantly understood.
"Yes!" I nodded.
"But that's not possible, right? He's a rich person, why would he put a fake vase in his home?" Xu Er said in confusion.
"Why?" I shook my head. "It's because of your little sister!" It wasn't his fault that he didn't understand. This wasn't a common occurrence in 1999, but even children knew about this several years later.
"Now that you said it, that really does seem like the case! Thinking about it, if he clearly knows that the vase would break, then why would he use a real one! A fake one can work the same!" Xu Er nodded.
"Precisely! That's why the fragment of the vase is the key to the issue!" I sad.
"Okay, then I'll go and ask for the fragments now!" said Xu Er.
"I'll go with you, you are too honest, and don't know the evilness of people!" I said. "I'm worried that he would make it difficult for you!"
Du Xiaowei drove the three of us to the place that Xu Er talked about.
Xu Er was right, this person was a rich person. The compound he was living in was a very high-class one. All the apartments inside were duplexes. What's more, I already knew beforehand that the person in question was called Lv Baoqiang.
Lv Baoqiang opened the door and was surprised to see three people. He didn't expect that Xu Er, a non-local, would find two helpers. However, he didn't think much of it, he didn't believe that this sort of certain thing would change anything.
"Xu Er, did you come to bring me the money or are you prepared to agree to my terms? My patience is limited!" said Lv Baoqiang.
"Mr. Lv, we came here this time to discuss about it with you!" I spoke up first.
"Who are you? Are your words of any use?" Lv Baoqiang looked at me and asked.
"I am the person Xu Er called over to deal with this. What I say represents what Xu Er says," I replied.
"Is that the case?" Lv Baoqiang looked at Xu Er.
Xu Er nodded.
Only then did Lv Baoqiang said unhappily to me, "Then let's discuss about what should be done!"
"Alright, you said that Xu Er's little sister broke a vase, where's the vase?" I asked directly.
"Vase? What are you asking about that for!" Lv Baoqiang looked at me vigilantly.
"You said that she broke a vase from the Ming Dynasty, I haven't even seen the vase, so why should I believe you?" I asked.
"Alright! Then I'll show you!" Lv Baoqiang turned around and went into the study, then took out a box, which he handed to me.
I opened to take a look. It was the fragments of a clay vase. Due to my work, I would often accompany my clients to antique auctions, so I did have some appraisal ability. Although I wasn't a professional, I was certain that the fragments in front of me wasn't something from the Ming Dynasty, which he claimed. This sort of low quality trash wasn't any different from the clay stuff that sold for two yuan by the streets!
Continue reading Chapter 168 – Vase Incident →
Chapter 167 – Pursuer
"Mhmm, he always use the excuse of guidance to get close to me, but fortunately, he didn't really get anything!" Su Yingzi said slightly proudly.
"It's no good even if he didn't get to take advantage of you. Which company is the movie from?" I remember Su Yingzi should have a contract with Chen Zelong!
"It's not the company's, it's a memorable historical movie that City N's city channel is shooting called "A Dream Back to the Ming Dynasty", the press has talked about it," said Su Yingzi.
I nodded, I did hear about such a film. It was directed by the famous director in the country, Deng Daqiang. And that bearded guy seems to be that Deng Daqiang! This guy wasn't anything good, he often uses the time of selecting new actors to do other deals. He was nearing 50, yet he often sleeps with seventeen or eighteen years old girls. However, this guy was way too brave, he actually dared to harass my woman!
"Ignore him. If he harasses you again, then call me," I said.
"He doesn't dare to do anything right now, no matter what, I'm still a famous celebrity, so he only dares to take advantage of me in words, and doesn't dare to actually do anything," said Su Yingzi.
"Being careful is better. You never know what he's thinking, god knows when his primal instincts would suddenly take over," I said.
Very soon, it was time for Su Yingzi to board. On the other hand, Xia Jing and I had to wait for another half an hour. After we said our farewells to Su Yingzi, Xia Jing asked me about some of Su Yingzi and my history, which I told her. When she heard that Su Yingzi had to marry Li Xiangdong, she couldn't help but think about her situation, instantly feeling like they're linked by the same problem, causing her to feel even closer to Su Yingzi.
As for me, I never would have thought that my one meeting with Su Yingzi actually caused a huge commotion. Of course, this is another story.
When we got to Yanjing city's airport, Xu Er was already there waiting for us. Xu Er did not have a car in Yanjing city, but fortunately, I had called Du Xiaowei beforehand, so he was already waiting for me at the airport.
When Xu Er saw the Santana 2000, he couldn't help but be surprised. He never thought that someone as rich as me would actually sit in such a common car.
"What? You got used to driving a Mercedes, and think that Santana is not enough?" I chuckled.
"Ugh…" Xu Er could only reply. "How could I? I was just think, Director Liu, you're so… Oh, is it because you don't want to be too braggy?"
"Hehe… Kind of," I didn't find it easy to explain either. Du Xiaowei had just randomly bought it.
"But don't underestimate this Santana 2000, this car has been altered, and is using the newest engine developed by East Asia Motion, the a03, so the speed and manipulability is not bad at all," Du Xiaowei explain.
Xu Er sat in the front, while Xia Jing and I sat in the backseat.
Just as Du Xiaowei was about to start the engine and back out of the parking spoke, a Honda Accord parked diagonally at the side, causing him to unable to get out at all.
"Beep beep!" Du Xiaowei pressed down on the car horn, but the Honda did not bulge, as if the driver was deaf.
Since there was no other choice, Du Xiaowei jumped off the car and knocked on the Honda Accord's window. Before Du Xiaowei said anything, the door opened, and a man jumped out. He pointed at Du Xiaowei and cursed, "**** your mom, the **** you knocking for!? What if you break it? Can you afford to compensate us!?"
"Please back the car out a bit, we have to go out," Du Xiaowei frowned as he looked at the vulgar upstart.
"If you're going out, then go. How the **** is it my business! It's your fault that your car is ******!" The upstart shook his head.
"Even a Mercedes, a BMW or a Chrysler can't get out when you're parked here!" Du Xiaowei felt rather angry.
At that moment, Xu Er also got off the car and walked over. When the upstart saw Xu Er's muscles, he couldn't help a bit scared. Since he thought that Xu Er was a fighter, he went back to his car bitterly, "I'll leave immediately."
"Wow, Xu Er, Xiao Wei couldn't deal with it, but you didn't say anything and the guy was already scared back to his car," I joked.
"That's because Xu-dage looks like a mob boss!" Xia Jing also laughed.
At that moment, Yanjing city wasn't very hot, and the temperature was just right when we opened the windows. The upstart driving the Honda Accord glanced at the people inside the Santana 2000 and noticed Xia Jing. That caused him to be immediately shocked and felt displeased. He thought to himself "****, I drive such a nice car, but I've never met such a pretty girl, yet the guy sitting in the Santana 2000 got such a beautiful one."
The more the upstart thought about it, the angry he got. People say that alcohol makes people braver, but the same could be said for lust. Although the upstart knew that he couldn't do anything about the girl, he could still cockily let out his displeasure.
After Du Xiaowei drove the car out, the upstart also immediately followed.
"Director Liu, the Honda Accord is following us," said Xu Er.
"Ignore it," I didn't mind that much.
I never would have thought that the Honda Accord actually accelerated to get in front of us and cockily beeped us with his car horn. That shocked Du Xiaowei, causing him to quickly step on the brakes.
Then, the Honda Accord surpassed us and left with a cocky laugh.
"What do we do?" Du Xiaowei's face was green in anger. He was already mad enough when he got off to argue, but he resisted back then, yet, at this very moment, he couldn't stand it any longer.
"Do whatever you want to do," I know Du Xiaowei wouldn't be content with not letting out his anger.
"Okay, Director Liu, you guys should hang on," Du Xiaowei shifted gears and stepped down on the pedal, causing the Santana 2000 to fly out.
The upstart, who was driving the Honda Accord, smiled proudly and excitedly as he listened to the radio, then suddenly saw the Santana 2000 appear behind him once again. What's more, it was speeding towards him with an incomparable speed. Furthermore, it seemed like it was going to over take him.
How could my Honda Accord not match up to his Santana 2000! The upstart was a bit speechless, thus, he stepped down on the pedal, causing his car to shoot out like a rocket.
However, Du Xiaowei did not show any weakness and remained hot on the Honda Accord's tail. Then, Du Xiaowei shifted gears, causing the Santana 2000 to overtake the Honda Accord in an instant.
The upstart pressed down on the pedal with all his might until blisters were about to pop out on his feet. However, he just couldn't catch up to the Santana 2000.
"Beep beep," A warning came from the Santana.
"The police is running speed tests one kilometer in front," Du Xiaowei looked at the warning machine and said, so he immediately decelerated the car.
However, when the upstart saw the Santana 2000 decelerate, he thought that Du Xiaowei was screwed, so he quickly pressed down hard on the pedal again, then quickly over took us.
"******," Du Xiaowei swore, then lowered the gears down by a notch once again.
As we expected, not long later, we saw two police in front holding cameras and speedometers, while the Honda Accord was parked at the side of the road with the upstart accepting the investigation in depression.
Continue reading Chapter 167 – Pursuer →
Chapter 166 – Meeting at the Airport
"Hehe, it was because we didn't book the tickets in advance. When we went to buy the tickets, only first class was left," I smiled.
Xia Jing wanted to say something, but a small commotion occurred at the entrance of the VIp lounge. A young woman wearing a small yellow jackets and sunglasses entered the VIP longue under the protection of two black-shirted people. Meanwhile, the ones causing the commotion were blocked outside the longue by the airport employees.
"Hmm? This person is so familiar," Xia Jin looked over at the young woman.
I couldn't help but smile wryly, she wasn't just familiar! This person was one of my secret girlfriends – Su Yingzi.
At this moment, there weren't many people in the VIP Longue. Aside from Xia Jing and I, only a few foreigners and old people were left. That was why Xia Jing and I were rather eye-catchy within the room. Su Yingzi clearly saw us as well, as she happily waved towards me.
Seeing Su Yingzi wave towards us, Xia Jing said in a bit of confusion, "Dagege, do you know her?"
After arriving in the VIP Longue, Su Yingzi no longer held herself back as much as when she was outside. She took off her sunglasses with a smile, and directly walked over to sit in the seat beside me.
"You're Su Yingzi!" Xia Jing exclaimed. For girls her age, the teenage star, Su Yingzi, had a pretty big impact on her.
Su Yingzi nodded with a smile, "Hello, I'm Liu Lei's… friend." However, her tone was clearly a bit jealous. Su Yingzi didn't feel good in her heart when she saw her boyfriend sit with other women.
"Ah! Dagege, you actually know superstar Su Yingzi!?" Xia Jing exclaimed. "Su-jiejie, can I call you that…"
"Of course," Su Yingzi smiled. Although Su Yingzi was a bit envious, but this girl was clearly leaning on Liu Lei, so that meant that they were very close, so the girl calling her jiejie wasn't wrong."
"That's great!" Xia Jing clapped happily. "Can I have a photo with you?"
Su Yingzi nodded, and asked for a digital camera from her bodyguards. I couldn't help but find it funny when I saw the Shuguang and Su's logo on the camera. It seems like Su Yingzi truly supports her family's business! Even the camera she is using is the product of her hubby and dad's company.
"What are you laughing at?" Su Yingzi was already slightly displease. Despite acting calmly towards Xia Jing, she let out all her anger on me after seeing me smile like an idiot.
I pointed at the logos on the camera.
"What's funny about that!?" Su Yingzi glared at me. "Are you saying that Su's isn't as famous as Shuguang?"
"I didn't mean that…" It seemed like Su Yingzi still didn't know that Shuguang Corporation was something of our family as well. She seemed rather displayed after products of her family's factories sticking on another company's logo.
Xia Jing looked at Su Yingzi and I in confusion, "Dagege, Su-jiejie doesn't seem very friendly towards you?"
Su Yingzi smiled after hearing that. However, she thought to herself in her heart, I'm unfriendly towards you. If you weren't here, god knows how friendly I would be with him! Yet, she still spoke up, "C'mon, let's take a photo."
Xia Jing only felt satisfied after happily taking many photos with Su Yingzi. After that, she looked over them on the digital camera over and over again.
"Wow! Now my classmates are going to be so jealous of me. They show off all the time with Su-jiejie's autograph, let's see what they're going to say now!" Xia Jing said excitedly. "Hmm? Who is this man? Is this Su-jiejie's boyfriend?"
Xia Jing pointed to the camera screen.
"Ah!" Su Yingzi took over the digital camera to have a look, then tried frantically to turn it off.
However, I already saw the photo. A large-bearded man was dipping his head and saying something to Su Yingzi. The two sat very closely with each other, and the mouth of the guy with a large beard was pretty much touching Su Yingzi's face.
My expression immediately darkened.
Su Yingzi felt rather frantic when she saw me turn angry.
All of a sudden, the atmosphere between us turned rather intricate.
Xia Jing looked at the two of us in confusion, "Dagege, what happened?"
"Nothing," I smiled. I thought about it and immediately understood. From the angle the photo was taken at, it was clear that someone had snapped the picture in secret. That's why Su Yingzi probably didn't know beforehand. From Su Yingzi's slightly frowning expression, I could also tell that she didn't have much feeling for the bearded guy.
However, Su Yingzi did not think so. When she saw that I suddenly smiled, she got even more frantic as she thought that I laughed because I was angry. Thus, she explained in a hurry, "There's nothing between him and I, seriously, I…"
It was fine when Su Yingzi didn't explain it. However, Xia Jing merely got more confused by the explanation, "Su-jiejie, what are you saying? You and dagege…"
"I… Ah… Actually…" Su Yingzi didn't want Xia Jing to know our relationship, but her explanation had instead exposed it.
"Alright, Xiao Zi, it's fine if you tell her," I said as I looked at the blushing Su Yingzi, and found it rather funny.
Xia Jing was no idiot. When she heard that I called Su Yingzi "Xiao Zi", then linked it with Su Yingzi's earlier expression and tone, she instantly understood. However, Xia Jing was not a woman that liked to fight over things, she also knew that I had a lot of women aside from her. What's more, since she has finally gotten with me, she naturally treasured our relationship. That's why, she didn't get rather annoyed, and instead said naturally, "It seems like I really wasn't wrong when I called you Su-jiejie just now!"
Seeing that the other person didn't display any hostility, Su Yingzi didn't find it wrong to get jealous anymore. What's more, she had no time to get jealous when she didn't explain the photo in the camera properly!
Thus, she replied openly, "Yeah, Xia Jing-meimei."
"Hehe, Xia Jing, now your classmates can't compete with you anymore. You guys might even have to sleep on the same bed in the future!" I teased.
"What are you saying, dagege, it's too embarrassing!" Xia Jing said embarrassedly.
"That photo…" Seeing that my mood improved by a lot, Su Yingzi decided to carefully raise the topic again.
"Oh, I already saw it. What does that beard guy do? Is he your pursuer?" I asked.
"He's the director of this movie I'm in… He's not really a pursuer, he's like that with all actress. I don't know which employee snapped the photo, I haven't seen it before," Su Yingzi explained.
"He, I knew it! How could our family's cute Xiao Zi fall for the beard? Wouldn't she get prickled to death by it when kissing!?" I laughed.
"You're terrible!" Su Yingzi said coquettishly. Su Yingzi had always been a good actress. At that moment, her voice was sexier than normal women, so even someone that had as much experience as me couldn't help but feel my heart waver.
Meanwhile, Xia Jing silently thought about what she was lacking in preparation to surpass the older sister in front of her in the future.
"Then he harassed you?" I asked.
Continue reading Chapter 166 – Meeting at the Airport →
Chapter 165 – Xu Er's Little Sister
"Judging from your ages, you guys should be still in school, right?" I asked.
"We… were expelled," said the three brats. "What's more, we don't want to go to school either, it's pointless."
"Going to school is to learn stuff, how can you say that it's useless!" Although I accept the logic of "all roads lead to Rome" and "masters come from all industries", it was still too early to enter the society for them. They might very well just waste their lives away."
"What use is being good at studying, you can still get expelled!" Wang Wei pointed to Zhang Yuliang and said. "He was originally one of the top ten students in our class!"
"Top ten!" I was a bit confused. "Why did someone in the top ten get expelled?"
"Our results weren't that bad either, it's just that the three of us beat up the dean's son…" said Wang Wei.
"You beat up the dean's son? What did you beat him up for?" I asked. However, I already made a judgement in my heart. This dean's son must be that sort of cocky person in school. I was once their age, and Liu Kesheng was a typical example of someone like that.
"He harassed Yu Feichao's girlfriend, since we're good bros with Yu Feichao, Zhang Yuliang and I beat him up in anger. We had just wanted to scare him, but he actually told his dad what happened, causing the situation to escalate… The three of us all come from single-parent families, our parents don't want us anymore, and since the dean saw that we didn't have any backing, he expelled the three of us," said Wang Wei.
I looked at Yu Feichao. The brat was kind of handsome, he was similar to what I was like when I was younger. "You already have a girlfriend at such a young age…" The moment I said that, I felt a bit speechless. Once upon a time, when I was their age, I already had two girlfriends, Zhao Yanyan and Chen Weier. When I looked at them, it was as if I saw an image of my past.
If I didn't know Uncle Zhao back then and didn't have any superpowers, I might have been bullied like them, right? If I didn't have any background, Liu Kesheng might very well have gotten me expelled.
Yu Feichao smiled embarrassedly, "We're childhood friends… But, I feel too embarrassed to go and find her now."
"If you guys can go back to school, are you guys still willing to return?" I asked. Actually, I just wanted to see if the three of them were truly helpless. If they gave up on themselves, then there was no helping it, I'll just get Ding Baosan to find somewhere for them to be subordinates at.
The three of them looked at each other, then finally nodded.
"But can we return?" I could tell that Yu Feichao wanted to return to school the most. He was probably still thinking about his little girlfriend.
"Of course, I smiled with a nod. "Since you guys want to go back to study, I can organize it for you guys. However, don't think about these sort of this in the society after going back to school. These aren't stuff that you guys should think about. Study properly, and you guys can work at Sanhouzi's company. Look, although he's part of the underworld, he's developing in a legal direction, so you guys shouldn't have the thoughts of getting famous at the bottom of society!"
"How about this, since your parents don't want you guys anymore, then follow Sanhouzi. Get him to find a place for you guys to stay and look over your studies!" I said.
"San-ye…" The three brats were still a bit scared of Ding Baosan.
"What San-ye! Just call him San-ge!" I said. After all, Ding Baosan wasn't much older than them.
After I handed them to Ding Baosan, I called Ye-bobo, who was also Ye Xiaoxiao's father to easily deal with the matter. No matter how amazing that dean was, he wouldn't dare to go against the Department of Education. Now that the dean knew that Yu Feichao and co. got a huge backing, he calmed down a little, while his son also stopped daring to do anything after seeing Yu Feichao and co. travel to and from school with bodyguards and cars like a young master of the underworld.
After dealing with it all, I also went back to Yanjing to go to school. I had promised Ding Wenfeng to join his something martial arts society. However, I didn't even participate in a single even in the end. That's why, he had called and told me that there was an international martial arts competition at the end of the year, so he wanted to discuss it with me. When I asked him why he wanted to discuss it with me, Ding Wenfeng smiled wryly and told me that I was the only member in the society outside of him, who was the president!
Right before I left, Ding Baosan drove me to the airport in person. During that, I talked to Ding Baosan about Xu Er's matters. This caused Ding Baosan to worry and complain about how Xu Er didn't tell him when something arose, so he couldn't help at all.
Just as we were talking about Xu Er's matters, my phone rang. It was actually a call from Xu Er.
"Director Liu, I'm Xu Er," said Xu Er.
"Xu-dage, how is the things on your side?" I asked.
"Director Liu, I caused you to worry…" Xu Er said hesitantly.
"Xu Er, are you in some sort of trouble? Whatever it is, just say it. As long as I can help, I will definitely do my best," I could tell that Xu Er had something else to say.
"… I just want to ask you to speak with Director Ding about whether it is possible to pay me my salary in advance…" Xu Er replied after a brief pause.
"Salary? You're lacking money?" I asked.
"Mm… My little sis got into some trouble. The other person wants me to compensate them for five hundred thousand yuan…" Xu Er said depressedly.
"Five hundred thousand? Why are they asking you to compensate for so much? Just what is going on? Tell me in detail!" I said.
Xu Er didn't make anything clear on the phone. However, when I asked him where he was, he told me that he was in Yanjing city. Thus, I told him to wait there for me, since I would be able to see him about three hours later.
As for little beauty Xia, I got her to return to Yanjing city with me. After all, her family will definitely worry after she has left for so long. I didn't want my relationship with those in-laws to be too tense. What's more, I didn't need to worry about them marrying Xia Jing to Lei Xiaolong. Recently, the Lei family's business was not going well in the US. Many large companies did not welcome Lei's Corporation and even cut communications with them.
Since the quickest flight wasn't a direct flight so we needed to fly to City N to transfer, and because Xia Jing and I sat in first class, we were put in the VIP lounge of City N's airport to rest after we landed.
"Dagege, there are so few people here!" Xia Jing laid intimately in my arms. Although I haven't eaten little beauty Xia for real yet, our nightly openness with one another caused us to be beyond close.
"Yeah, actually, there aren't a lot of people that fly first class. Aside from some people with their backgrounds, nobody would spend so much pointless money to fly first class. What's more, many first class seats didn't have any discount, so they were nearly double the price of economy. Hehe," I replied.
"Then let's fly economy from now on," said Xia Jing.
Continue reading Chapter 165 – Xu Er's Little Sister →
Very Pure & Ambiguous - The Prequel, ~News~
Even more Very Pure and Ambiguous: The Prequel Updates
Hey everyone, Selutu here. Sorry for the long absence here, like I said in the previous update, somethings came up, so I had to put the novel on hiatus for a while.
Well, I'm sure you guys know what kind of crap Qidian International did this time, right? So I'll cut straight to it. This weekend, I'll be releasing a load of chapters (well, less than the other mass releases I've done), then we'll cut it at that.
It's been a great run and I really don't want to drop the novel, but honestly, the kind of stuff Qidian International is pulling is really getting me worried. I don't want to get dragged into that sort of mess.
So… tata for the most part. Although, I will be posting fanfics of the novel, so you guys might want to read that. winks