text:
\section{Introduction}

Travel time studies in urban areas show that 12--55\% of commute travel time is due to delays induced by signalized intersections (stopped or approach delay)~\cite{levinson1998speed,tirachini2013estimation}. Hence, optimized signal controllers have the potential of reducing commute time, traffic congestion, emissions, and fuel consumption, while requiring minimal infrastructure changes.

Recent publications~\cite{laval2019large,li2016traffic,van2016coordinated}
proposed to utilize state--of--the--art reinforcement learning algorithms for online optimization of signal controllers.
Such previous work showed a potential reduction of up to 73\% in vehicle delays when compared to fixed--time actuation~\cite{mousavi2017traffic}.
Despite showing compelling empirical results, the controllers defined in such previous work have little applicability in the real world since their underlying control function is based on a deep neural network (DNN). While providing flexible and powerful function approximations, DNNs lack an interpretable inference process~\cite{samek2017explainable} which might prevent the implementation of related controllers in practice.
Given their liability for drivers' safety and mobility, governmental transportation agencies are conservative in requiring that signal controllers are interpretable and regulated. Consequently, this paper focuses on defining and studying self--optimizing, regulatable signal control policies.

The following contributions are made, which, to the best of our knowledge, were not addressed in previous work:

\noindent
\begin{enumerate}

\item Define and justify a regulatable control function for the signal control domain.

\item Define and study the effectiveness of a domain specific regulatable function when compared to a DNN--based policy.

\item Study the effectiveness of different optimization methods for online training of a signal control policy. Namely, \textit{Covariance Matrix Adaptation Evolution Strategy} (\textsc{cma--es})~\cite{hansen2001completely}, \textit{Proximal Policy Optimization} (PPO)~\cite{schulman2017proximal}, and \textit{Deep Q--learning} (DQN)~\cite{mnih2015human}.

\item Develop three variants of the DQN algorithm that utilize and train a regulatable control function. These variants are denoted \textit{Deep Regulatable Q--learning} (DRQ), \textit{deep regulatable softmax Q-learning} (DRSQ), and \textit{deep regulatable hardmax Q--learning} (DRHQ).

\item Test the performance of the aforementioned optimization methods through computer--based simulation of a real--life intersection and observed demand.

\item Compare the proposed methods with the commonly deployed \textit{actuated signal controller}~\cite{bonneson2011traffic}. As opposed to previous work that were compared to, the less effective, fixed--timing actuation policy.
\end{enumerate}

We show that a designed regulatable control function can reduce traffic delays by up to 30\% when compared to common actuated signal controllers. Moreover, such a regulatable control function is shown to be competitive with the performance of a DNN based controller.
Next, we turn to study online optimization approaches for signal controllers. Our empirical study shows that a value--based approach (specifically DQN) converges faster, and to a better policy, compared to a policy--gradient approach (specifically PPO). Our regulatable Q--learning variant, DRHQ, is shown to result in a policy that is competitive with commonly deployed actuated signal controllers after a single training episode. The control policy is further improved in successive episodes, reaching up to 19.4\% reduced delays

\section{Background and Related Work}

This section provides the necessary background which includes the domain description, Markov decision processes, reinforcement learning, and related work.

\subsection{The traffic signal domain}

A signalized intersection is composed of incoming and outgoing roads where each road is affiliated with one or more lanes.
Each signalized intersection is assigned a set of phases, $\Phi$. Each phase, $\varphi \in \Phi$, is affiliated with a specific traffic movement through the intersection, as illustrated in Figure~\ref{fig:phases}.
Two phases are defined to be in \textit{conflict} if they cannot be enabled simultaneously (their affiliated traffic movement is intersecting).
For example, in the phase allocation presented in Figure~\ref{fig:phases}, $\varphi_2 \text{ and } \varphi_1$ are conflicting phases.

\begin{center}
\begin{figure}[t]
    \includegraphics[trim=0 0 0 0, clip, width=0.85\columnwidth]{phases.png}
    \caption{Common phase allocation for a 4--way intersection.}
    \label{fig:phases}%
\end{figure}
\end{center}
\subsection{Reinforcement learning}

In reinforcement learning (RL) an agent is assumed to learn through interactions with the environment. The environment is commonly modeled as a Markov decision process (MDP) which is defined by: $\mathcal{S}$ -- the state space, $\mathcal{A}$ - the action space, $\mathcal{P}(s_t,a,s_{t+1})$ -- the transition function of the form $\mathcal{S} \times \mathcal{A} \times \mathcal{S} \rightarrow Prob$, $R(s,a)$ -- the reward function of the form $\mathcal{S} \times \mathcal{A} \rightarrow \mathbb{R}$, and $\gamma$ -- the discount factor. The agent is assumed to follow an internal policy $\pi$ which maps states to actions, i.e., $\mathcal{S} \rightarrow \mathcal{A}$. The agent's chosen action ($a_t$) at the current state ($s_t$) affects the environment such that a new state emerges ($s_{t+1}$) as well as some reward ($r_t$) representing the immediate utility gained from performing action $a$ at state $s$, given by $R(s,a)$. The observed reward is used to tune the policy such that the expected sum of discounted reward, $J_\pi = \sum_t \gamma^t r_t$, is maximized. The policy $argmax_\pi[J_\pi]$ is the optimal policy denoted $\pi^*$.

Recent publications~\cite{van2016coordinated,shabestary2018deep} suggest applying one RL approach, Deep Q--learning (DQN)~\cite{mnih2015human}, for training and operating signal controllers. In DQN, a deep neural network (DNN) is trained to map state--action pairs to a real number denoted the $Q$--value i.e., $\mathcal{S} \times \mathcal{A} \rightarrow \mathbb{R}$. The $Q$--value for a given state--action pair, $(s,a)$, represents the sum of discounted reward after performing $a$ at $s$ and then following the optimal policy ($\pi^*$). Algorithm 1 presents the pseudocode for DQN (for now, ignore lines in red i.e., Lines~\ref{ln:init_G},~\ref{ln:set_aG},~\ref{ln:begin_forn}--\ref{ln:end_forn}).
For each training episode, DQN sets the initial state in Line \ref{ln:init_seq}. Next, for each time step within an episode, an action is chosen in an epsilon greedy manner (Lines 8--9) where the greedy action is the one with the maximal $Q$--value as approximated by the Q--function. The chosen action is executed and the outcome (immediate reward and next state) is stored in the replay memory (Lines 11--12). Next, a minibatch of transitions (state, action, immediate reward, next state) is sampled and the Q--function weights ($\theta$) are updated such that the squared error from the target $Q$--value, $y_j$, is reduced (Lines 13--15). The target $Q$--value ($y_j$) is computed using the temporal difference propagated from the next state~\cite{singh1996reinforcement}. Finally (Line \ref{ln:tar_upd}), every $C$ steps, the target Q--function ($\hat{Q}$) is set to equal the trained Q--function ($Q$). Doing such a delayed update is justified by Mnih et al.~ \cite{mnih2015human} as a way to reduce oscillations or divergence of the policy.

\subsection{Related work}

Previous works have examined reinforcement learning algorithms for online optimization of signal controllers. Unfortunately, the applicability of these protocols is questionable due to various drawbacks: \textbf{(a)} long and unsafe tuning process~\cite{van2016coordinated,shabestary2018deep,liang2018deep,mousavi2017traffic,genders2016using
, \textbf{(b)} cumbersome policies that cannot be easily interpreted and regulated (mostly relying on deep artificial neural networks) thereby providing limited liability~\cite{van2016coordinated,shabestary2018deep,liang2018deep,mousavi2017traffic,genders2016using,wei2018intellilight,li2016traffic
, \textbf{(c)} limited scalability (with regards to the number of managed phases)~\cite{van2016coordinated,wei2018intellilight,mousavi2017traffic,li2016traffic
, \textbf{(d)} reliance on (currently) unrealistic traffic sensing capabilities~\cite{wei2018intellilight,mousavi2017traffic}, \textbf{(e)} experiments evaluated on unrealistic scenarios such as synthetic traffic demand~\cite{mousavi2017traffic,bakker2010traffic,genders2016using,li2016traffic,liang2018deep,abdoos2011traffic,prabuchandran2014multi
, and handling only through traffic (no turning vehicles)~\cite{mousavi2017traffic,li2016traffic}.

A line of previous work
~\cite{wang2019large,laval2019large,prabuchandran2014multi, aslani2018traffic,van2016coordinated} focused on controlling and coordinating a set of signal controllers. The goal of such work is optimizing traffic flow over a road network that consists of several signalized intersections. Such a multiagent control manifests a combinatorial action space which results in limited scalability as well as slow and inefficient learning.

While some of the aforementioned publications presented compelling results, none yielded a control function that is human interpretable and liable. Given liability and regularity constraints faced by governmental transportation agencies, signal control functions will most likely not be adopted unless they can be interpreted and regulated.

Interpretability of deep reinforcement learning has recently been examined. Similar to this work, techniques for mimicking deep neural networks with  interpretable actuation were suggested. One work~\cite{liu2018toward} suggests using a variant of \textit{continuous U trees}, however this approach requires that the affiliated deep Q--network be trained to convergence initially. As a result, the control policy remains uninterpretable for the duration of the training episodes. Consequently, such an approach cannot be utilized in safety critical domains that require online learning.
This shortcoming is shared by other works \cite{hein2018interpretable, verma2018programmatically}, each requiring learning a model that is based on a deep neural net prior to utilizing an interpretable controller.

\section{Problem Definition}
\label{sec:prob-definition}

The focus of this paper is around online optimization of a human interpretable signal controller. Following previous work ~\cite{shabestary2018deep, wei2018intellilight, prashanth2011reinforcement} we model this problem as an MDP. The state space is defined by the possible input assignments. The set of available actions per state is defined by the possible output assignments and constraints. Constraints are considered as part of the transition function of the environment. For example, actions do not include yellow lights, these are set automatically when required for safety. The transition function is provided by the environment. Reward is defined by reduction in accumulated traffic delay. The controller's operation is defined as follows:

\noindent
\textbf{Input:} \textbf{(1)} current signal assignment (green, yellow, and red assignment for each phase), for each lane: \textbf{(2)} number of approaching vehicles, \textbf{(3)} stopped vehicles accumulated waiting time, \textbf{(4)} number of stopped vehicles, and \textbf{(5)} average speed of approaching vehicles.
Note that all inputs are necessarily non--negative.

\noindent
\textbf{Output:} next signal assignment for a duration of time equal to a given minimum phase length. Signal assignments are abstracted to phases which group individual assignments into traffic movements. Non-conflicting pairs of phases give a complete signal assignment.

\noindent
\textbf{Constraints:} \textbf{(1)} right--of--passage cannot be assigned to conflicting phases, \textbf{(2)} a yellow signal must appear for a predefined time interval between red and green signals.

\noindent
\textbf{Assumptions: }
\textbf{(1)} the intersection's layout is known, i.e., incoming/outgoing lanes, phases, and conflicting phases, \textbf{(2)} real--time sensing of incoming traffic as specified in the problem input.
These sensing assumptions are reasonable given latest advances in traffic sensing technology, namely, radar~\cite{samczynski2011concept}, Wi--Fi scanning~\cite{kostakos2013traffic}, drivers' smartphones~\cite{mohan2008nericell}, connected vehicles~\cite{wan2016mobile}, image processing~\cite{bautista2016convolutional}, infrared sensing~\cite{iwasaki2011robust}, and Synthetic Aperture Radar (SAR) satellites~\cite{meyer2006performance}.

\noindent
\textbf{Desiderata: }
The signal controller is expected to assign right--of--passage such that: \textbf{(1)} the average delay suffered by incoming vehicles is minimized \textbf{(2)} the actuation policy can be interpreted and regulated by a human (a precise definition is given next).

\subsection*{Regulatable signal controller policy}

Control of safety critical tasks in general, and signal control specifically, require interpretable policies for liability and performance guarantee purposes. Unfortunately, there is little agreement on the meaning of interpretability. Ahmad et al~\citeyear{ahmad2018interpretable} state that ``The choice of interpretable models depends upon the application and use case for which explanations are required". Consequently, this section provides a definition for an interpretable model for the signal domain.

A signal control policy is defined as, $\pi(s;\theta) \rightarrow (\Phi^g, \Phi^y, \Phi^r )$, mapping a given traffic state, $s$, and a set of parameters, $\theta$, to a signal assignment, that is three sets of phases representing green, yellow, and red signal assignments, $\Phi^g,~ \Phi^y,~ \Phi^r$, respectively. A valid signal assignment is one that results in no conflicting traffic movements.  The traffic state, $s$, is defined by the provided sensors input. The control function's parameters, $\theta$, should be tuned such that the control policy yields optimized performance.

We define a precedence function, $g(s,\Phi;\theta') \rightarrow \mathbb{R}$, mapping a given traffic state, $s$, a set of parameters, $\theta'$, and a non--conflicting set of signal phases, $\Phi$, to a real number representing the precedence of assigning right--of--passage to $\Phi$. Note that a $Q$-function~\cite{mnih2015human} could serve as a precedence function but a precedence function is not necessarily a $Q$-function.
Such a precedence function suggests a control policy where the chosen signal assignment in state $s$, is ${\text{argmax}_{\Phi}}g(s,\Phi;\theta')$. A series of precedence functions, $G$, one for each action, defines a full order over all phase assignments. $\theta'$ is tuned such that the action with the highest precedence is the optimal action in expectation.

\begin{defn}[Regulatable precedence function] \label{def:regulatable}
A precedence function $g$ is defined as regulatable, if for all state variables $s[i] \in s$, $\frac{\partial g}{\partial s[i]}$ exists and is either non--negative or non--positive for any possible assignment of $s$, i.e., the precedence function is monotonic in the state variables.
\end{defn}

A control policy that is based on a regulatable precedence function is defined as a \textit{regulatable control policy}.
For such a policy, changes to the signal assignment can be intuitively interpreted as following changes in state variables, e.g., the right--of--passage was revoked from $\varphi_2$ and granted to $\varphi_4$ (as defined in Figure~\ref{fig:phases}) because the number of stopped Southbound vehicles increased while the number of such Eastbound vehicles decreased. Moreover, should policy adjustment be required, adding a weighting parameter to each state variable allows for intuitive tuning of the control function with regards to the specific state variable.

Consider the example in Figure~\ref{fig:interp}, traffic has accumulated on the Eastbound left--turn lane, however the light configuration has not yet been switched. A simple regulatable control function can be optimized which chooses between two configurations, allowing Westbound traffic, or allowing Eastbound traffic. In this example, the precedence function is a simple summation of 4 input variables (Queue length, number of approaching vehicles, accumulated waiting time for stopped vehicles, and average speed). Each of these variables is affiliated with one tunable parameter. E.g., ``W-Through Queued" specifies the weight factor affiliated with the queue length variable for the Westbound trough lanes. We later generalize and discuss this type of controller in greater detail.
The table at the bottom of the figure specifies optimized values for the different tunable parameters.
By inspecting the values assigned we can observe that the speed of approaching vehicles on the Westbound left--turn lane is the primary factor in the decision to maintain the current light configuration. However, it might be the case that the Eastbound left--turn lanes are too short to accommodate the typical number of vehicles. The parameters can be easily tuned to give higher precedence to clearing traffic from these lanes by increasing the weight parameter E-Left Queued.

\begin{figure}[h]

      \subfloat{  \includegraphics[width=0.9\columnwidth]{interp.png}}

    \\ \

    	\subfloat{\small
    	\centering
    	\begin{tabular}{|l|r|r|r|r|r|}
    	    \hline
    		Action & Queued & Approaching & Waiting (s) & Speed & Value\\
    		\hline
    		W--Through & 0.0 & 2.1 & 0.0 & 0.33 &\\
    		W--Left & 3.64 & 0.77 & 4.65 & 11.04 & 22.53\\
    		\hline
    		E--Through & 1.25 & 0.0 & 0.73 & 0.0 &\\
    		E--Left & 5.32 & 1.67 & 10.34 & 0.0 & 19.31\\
    		\hline
        \end{tabular}
    	\normalsize}

	\caption{Optimized actuation policy for the presented intersection (top). The policy is based on a linear control function that assigns values to input variables presented in the table (bottom).}

	\label{fig:interp}
\end{figure}

This work makes the assumption that a regulatable control function is interpretable (a similar assumption was made in~\cite{hein2018interpretable}).

\section{Designing a Regulatable Precedence Function}

Based on the problem input (as defined in the problem definition) we define the following, phase dependent state variables, $s_\varphi[1,...,6]$: (1) number of stopped vehicles, (2) number of approaching vehicles, (3) cumulative stopped time, (4) average stopped time, (5) average queue length = stopped vehicles divided by the number of lanes, and (6) average speed for approaching vehicles. These phase dependent variables relate only to vehicles that are present on lanes served by phase $\varphi$. For example, $s_{\varphi_1}$ returns the number of stopped vehicles on the Eastbound left--turning lanes.
On top of these state variables, the proposed precedence function penalizes phase assignments that enforce clearance intervals. For instance, activating (assigning right--of--passage) the Southbound through phase straight after an active Eastbound through phase commonly requires a clearance interval. The clearance interval activation and duration is intersection dependent. For general guidelines see~\cite{bonneson2011traffic}.
Transitioning from a currently active phase set to another phase set, $\Phi$, triggers one of the following cases.
\begin{enumerate}
\item \textbf{Full clearance} -- an interval where no phase is active (all red).
\item \textbf{Partial clearance} -- an interval where part of the phases in $\Phi$ are inactive (prior to becoming active).
\item \textbf{Permissive clearance} -- a short interval (shorter than in the partial clearance case) where part of the phases in $\Phi$ are inactive. Commonly due to a currently active permissive--left phase (when applicable).
\item \textbf{No clearance} -- no clearance is required.
\end{enumerate}
Each of these cases is affiliated with a flag, denoted $f_{1,...,4}$, that is set to 1 for the active case and 0 for the others. In a state where two cases are simultaneously active, only the one with the lowest index will be set to 1.

Our proposed precedence function can now be defined as:
\begin{equation} \label{eq:prec}
    g(s,\Phi ; \theta') = \sum_{\varphi \in \Phi} \sum_{i=1}^{6} \left( w^{\Phi\varphi}_i s[i] \right) ^{p^{\Phi\varphi}_i} \cdot \sum_{j=1}^{4} \left( w'^{\Phi}_j f_j \right) ^{p'^{\Phi}_j}
\end{equation}

Where $\theta' = \{w,p,w',p'\}$.
In total, the designed function is composed of 6 state variables per $\varphi$ in $\Phi$, each with two affiliated tunable parameters, a weight $(w^{\Phi\varphi}_{1...6})$ and an exponent $(p^{\Phi\varphi}_{1...6})$. Also, each of the clearance flags ($f_{1...4}$) is affiliated with two tunable parameters, a weight $(w'^{\Phi}_{1...4})$ and an exponent $(p'^{\Phi}_{1...4})$. Each phase combination ($\Phi$), phase ($\varphi$), and index ($i$ or $j$) defines a unique tunable parameter.
All together, this function defines 12 parameters per phase within a phase combination ($w^{\Phi\varphi},~p^{\Phi\varphi}$) and 8 parameters per phase combination ($w'^{\Phi},~p'^{\Phi}$).
In the phase diagram defined in Figure~\ref{fig:phases} there are 8 phases which result in 8 sets of non--conflicting pairs. Namely, $\{(\varphi_1,\varphi_2) \bigtimes (\varphi_5,\varphi_6) \cup (\varphi_3,\varphi_4) \bigtimes (\varphi_7,\varphi_8)\}$. Consequently, the appropriate regulatable function $G$ (over all 8 phase pair precedence functions) will be composed of $8 (2 \cdot 12 + 8) = 256$ tunable parameters.

\begin{lem}
  \label{lem:regulatable}
    The precedence function defined in Equation~\ref{eq:prec} is regulatable according to Definition~\ref{def:regulatable}.
\end{lem}
\begin{proof} \ \\
$$\frac{\partial g}{\partial s[i]} = w^{\Phi\varphi}_{i} p^{\Phi\varphi}_i s[i]^{p^{\Phi\varphi}_i - 1}  \cdot \sum_{j=1}^{4} \left(w'^{\Phi}_j f_j\right)^{p'^{\Phi}_j}$$
$s[i] \ge 0$ for any $i \in \{1,...,6\}$ (see `input' in problem definition).
Consequently, $s[i]^{p^{\Phi\varphi}_i - 1} \ge 0$. All other components of the partial derivative i.e.,  $w^{\Phi\varphi}_{i} p^{\Phi\varphi}_i  \cdot \sum_{j=1}^{4} \left(w'^{\Phi}_j f_j\right)^{p'^{\Phi}_j}$, are not dependent on any of the state variables and can be viewed as a constant ($\vartheta$). As a result, $\frac{\partial g}{\partial s[i]}$ would be either non--negative ($\vartheta \ge 0$), non--positive ($\vartheta \le 0$), or both ($\vartheta = 0$),  for any $i,~s$ and a given $\Phi,~\theta'$ assignment.

\end{proof}

The precedence function defined in Equation~\ref{eq:prec} is hereafter denoted the \textit{designed precedence function}. The affiliated control policy which returns $\text{argmax}_\Phi G(s,\Phi; \theta')$ is hereafter denoted the \textit{designed control policy}. Next, we discuss general techniques for online tuning of $\theta'$ such that the performance of any regulatable control policy (and specifically the designed control policy) is optimized.

\begin{algorithm}[t!]
\label{alg:DRQ}
\SetAlgoLined
\DontPrintSemicolon
Initialize replay memory $D$ to capacity $N$\;
Initialize $Q$ function with random weights $\theta$\;
Initialize target $\hat{Q}$ function with weights $\bar{\theta}=\theta$\textcolor{red}{\;
Initialize regulatable function $G$ with weights ${\widetilde{\theta}} = [1,...,1]$} \label{ln:init_G}\;
    \For{$episode=1$ \KwTo $M$}{
        Initialize $s_1$ with observed state \label{ln:init_seq} \;
    	\For{$t=1$ \KwTo $T$}{
        	With probability $\epsilon$ select a random action $a_t$ otherwise select:\;
        	~~~~$a_t= \text{argmax}_{a}Q(s_t,a;\theta)$ \# for DQN\textcolor{red}{ \label{ln:set_aQ}\;
        	~~~~$a_t= \text{argmax}_{a}G(s_t,a;\widetilde{\theta})$ \# for regulatable} \label{ln:set_aG}\;

        	Execute action $a_t$ and observe reward $r_t$ and state $s_{t+1}$\;

        	Store transition ($s_t,a_t,r_t,s_{t+1}$) in $D$\;

        	Sample random minibatch of transitions $(s_j,a_j,r_j,s_{j+1})$ from $D$\;

        	Set $y_j=r_j+\gamma\text{max}_{a'}\hat{Q}(s_{j+1},a';\bar{\theta})$\;

        	Perform a gradient descent step on $(y_j-Q(s_j,a_j;\theta))^2$ with respect to $\theta$\;

        	Every C steps reset $\hat{Q}=Q$ \label{ln:tar_upd} \;

        	\BlankLine\color{red}
        	    \For{$n=1$ \KwTo $N$}{ \label{ln:begin_forn}
            	    Sample random minibatch of transitions $(s_j,a_j,r_j,s_{j+1})$ from $D$ \label{ln:sample} \;

                	\Case{\textbf{DRQ}}{
                    	Set $y_j=r_j+\gamma\text{max}_{a'}\hat{Q}(s_{j+1},a';\theta)$ \label{ln:drq_bellman} \;
                    	Perform a gradient descent step on $(y_j-G(s_j,a_j;\widetilde{\theta}))^2$ with respect to $\widetilde{\theta}$ \label{ln:drq_opt} \;
                	}
                	\Case{\textbf{DRSQ}}{ \label{ln:drsq}
                	    Set $X_j = \text{softmax}_a\left(Q(s_j,\cdot ~;\theta)\right)$\;
                    	Set $Z_j = \text{softmax}_a\left(G(s_j, \cdot ~;\widetilde{\theta})\right)$\;
                    	Perform a gradient descent step on $-\sum_{a \in A}X_j[a]log(Z_j[a])$  w.r.t. $\widetilde{\theta}$ \label{ln:drsq_opt} \;
                	}
                	\Case{\textbf{DRHQ}}{ \label{ln:drhq}
                	    Set $X_j=\begin{cases}
                	        1 & \text{argmax}_{a'}Q(s_j,a';\theta)\\
                	        0 &  a \in A\setminus \{a'\}
                	    \end{cases}$ \label{ln:drhq_cases} \;
                	    Set $Z_j =  \text{softmax}_a\left(G(s_j, \cdot ~;\widetilde{\theta})\right)$\;
                    	Perform a gradient descent step on $-\sum_{a \in A}X_j[a]log(Z_j[a])$  w.r.t. $\widetilde{\theta}$ \label{ln:drhq_opt} \;
                	}
            	}\label{ln:end_forn}
    	}
    }
	\caption{DQN and \textcolor{red}{3 DRQ variants shown in red}}
\end{algorithm}

\section{Parameter Tuning}

A line of publications~\cite{wei2018intellilight,liang2018deep,van2016coordinated} reported that the DQN algorithm is particularly suitable for online signal control optimization. Unfortunately, the underlying policy in DQN is not regulatable as it is based on a DNN (Line \ref{ln:set_aQ} in Algorithm 1). In order to bridge this gap, we suggest training a given regulatable policy function of the type $\text{argmax}_a G(s,a;\widetilde{\theta})$, to imitate the Q--network actuation i.e., $\text{argmax}_{a}Q(s,a;\theta)$. A simple approach would be to directly use Q--learning with a function approximator that is defined as the regulatable function. However, doing so was found to make reaching a reasonable policy infeasible under even artificially low demands. Such a simple function approximator is incapable of representing the required intermediate functions for learning action values over an extended period of time.

Consequently, other approaches for leveraging DQN to optimize regulatable policies are considered.
These approaches follow the pseudocode described in Algorithm 1. The lines in red (Lines~\ref{ln:init_G},~\ref{ln:set_aG},~\ref{ln:begin_forn}--\ref{ln:end_forn}) show the required additions on top of the original DQN algorithm. It is important to note that instead of selecting an action according to $\text{argmax}_{a}Q(s,a;\theta)$ (Line \ref{ln:set_aQ}), the regulatable version selects an action according to a regulatable policy, $\text{argmax}_a G(s,a;\widetilde{\theta})$ where $G$ is a previously initialized regulatable function (Line \ref{ln:init_G}). Replacing the actuator in the DQN algorithm (Line \ref{ln:set_aG} in lieu of Line \ref{ln:set_aQ}) is reasonable as DQN is an \textit{off--policy} algorithm, i.e., training the Q--function does not require that the same function interacts with the environment. Moreover, the powerful (DNN based) Q--function approximation that is trained by DQN can be used to train the regulatable function, $G$. Consequently, $G$ is repeatedly trained to minimize the error between $\text{argmax}_a G$ and $\text{argmax}_a Q$. This training is performed at every time step over $N$ random minibatches from the replay memory (Lines \ref{ln:begin_forn},\ref{ln:sample}). Next, we discuss 3 different strategies for training $G$.

\subsection{Deep Regulatable Q--Learning (DRQ)}

DRQ is our basic Regulatable Q--Learning variant where the parameters of the regulatable function $\widetilde{\theta}$ are tuned towards equivalency between $G$ and the $Q$--function (using SGD with a squared loss function over the provided minibatch, Line \ref{ln:drq_opt}).
This variant follows the fact that if $G(s,a) = Q(s,a)$ for all $(s,a)$ then the required policy equivalency is achieved i.e., $\forall s, \text{argmax}_{a}G(s,a) = \text{argmax}_a Q(s,a)$.

Attempting to tune $\widetilde{\theta}$ such that the regulatable function $G$ would match the DNN--based $Q$--function may not be feasible in many cases. A regulatable function is more constrained and possesses far fewer tunable parameters compared to a DNN. As a result, a DNN is usually able to approximate a much larger set of functions compared to a regulatable approximator. Indeed, our empirical study found that the designed precedence function is very limited in its ability to approximate $Q$--values.
However, setting the regulatable function, $G$, to imitate the action selection of DQN does not require that $G(s,a) \equiv Q(s,a)$. This understanding leads us to DRQ variants that provide extra flexibility with regards to the function approximated by $G$.

\subsection{Deep Regulatable Softmax Q--Learning (DRSQ)}

In DRSQ the parameters of the regulatable function $\widetilde{\theta}$ are tuned towards \textbf{proportional} equivalency between $G$ and the $Q$--function. This variant follows the fact that if $G(s,\cdot) \propto Q(s,\cdot)$ for all $s$ then the required policy equivalence is achieved i.e., $\forall s, \text{argmax}_{a}G(s,a) = \text{argmax}_a Q(s,a)$.
The proportionality values over all actions are standardized using the softmax function. As a result the SGD applied for tuning $\widetilde{\theta}$ takes a gradient based on the cross--entropy objective i.e., using the log loss function (Lines \ref{ln:drsq}--\ref{ln:drsq_opt}).

\subsection{Deep Regulatable Hardmax Q--Learning (DRHQ)}

DRHQ stems from the understanding that policy equivalency does not require full equivalency or even proportional equivalency between $G$ and the $Q$--function. In fact, for achieving policy equivalency it is sufficient to tune $\widetilde{\theta}$ directly towards $\text{argmax}_{a}$ equivalency between $G$ and the $Q$--function. This can be achieved by setting the target value for a given $s, a$ pair as 1 for $\text{argmax}_a Q(s,a)$ or 0 otherwise (Line \ref{ln:drhq_cases}). Next, SGD is used to tune $\widetilde{\theta}$ according to the log loss between the target values and $\text{softmax}\left(G(s,\cdot)\right)$.

\subsection{Other tuning approaches}

The covariance matrix adaptation evolutionary strategy (\textsc{cma--es}) algorithm~\cite{hansen2001completely} is known to be a particularly effective parameter tuning approach. \textsc{cma--es} is specifically suitable for domains where the tunable parameters have a continuous value range. Moreover, \textsc{cma--es} is known for having few hyper--parameters with fairly low sensitivity. As a result, it is particularly appealing for testing and validating our designed policy. On the other hand, \textsc{cma--es} may be unsuitable for online tuning due to inefficient data sampling, requiring several full episodes for a single policy update step. Moreover, the erratic exploration of \textsc{cma--es}, though helpful in avoiding local optimums, is less suitable for safety critical domains.

Another natural candidate for online parameter tuning is the policy gradient approach~\cite{sutton2018reinforcement}. Specifically, the Proximal--Policy Optimization (PPO) algorithm~\cite{schulman2017proximal} is particularly suitable for safety--critical domains as it encourages bounded policy gradient steps. PPO achieves this behaviour by clipping the expected advantage for large policy divergence. Consequently, PPO is expected to result in smooth and steady convergence but, on the other hand, is more prone to settle in a local optimum and achieve sub--optimal performance.

\section{Empirical Study}
The purpose of the empirical study is to evaluate and analyze the performance of the proposed designed control policy along with the affiliated online tuning algorithms. Specifically, the empirical study aims at answering the following questions:\\
\begin{enumerate}
    \item Can the designed control policy approximate a deep Q--learning optimized policy

    \item Is a policy gradient optimization approach suitable for training the designed control policy?

    \item How do the regulatable Q--learning variants compare to the state--of--the--art, DQN--based signal controller?
\end{enumerate}

\subsection{Experimental settings}

\begin{figure*}%
    \centering
    \subfloat{{\includegraphics[height=4.3cm]{Signal7157.png} }}%
    \subfloat{{\includegraphics[height=4.3cm]{sumo.png} }}%
    \caption{A signalized intersection at State St \& E 4500 S, Murray, Utah (left), (picture credit: Google Maps), and an equivalent intersection modeled in SUMO (right).}%
    \label{fig:UDOT}%
\end{figure*}

The reported experiments rely on a well--established traffic simulator, Simulation of Urban MObility (SUMO)~\cite{behrisch2011sumo}, along with traffic scenarios that are based on real--life observations. The Utah department of transportation (UDOT) provides an open access database (\url{https://udottraffic.utah.gov/ATSPM}) specifying the observed traffic demand for 2092 signalized intersections. The database specifies the number of vehicles affiliated with each incoming, outgoing road combination in 5 minutes aggregation. The demand reported by UDOT is parsed into SUMO where vehicles are spawned with equal probability along equivalent 5 minute intervals.

\begin{table}
    \label{tab:demands}
	\small
	\centering
	\begin{tabular}{|l|r|r|r|r|}
	\hline
		Demand & \multicolumn{1}{|c|}{Total} & \multicolumn{1}{|c|}{Avg Rate} & Low Rate & High Rate\\
		\hline
		Low & 45,112 & 1.04 & 0.59 & 1.29\\
		Medium & 51,298 & 1.19 & 0.76 & 1.42\\
		High & 61,261 & 1.42 & 0.98 & 1.59 \\
		\hline
    \end{tabular}
	\normalsize

	\caption{Traffic demand for three different days representing low, medium, and high traffic volumes. Each day is affiliated with: the total number of approaching vehicles (Total), approaching vehicle per second on average (Avg Rate), during off--peak (Low Rate), and during peak hour (High Rate).}
\end{table}

The reported results relate to a representative major intersection, State St \& E 4500 S, Murray, Utah. Source code for all experiments is available at: \textit{https://github.com/jault/StateStreetSumo}. This intersection is chosen as it is affiliated with high volumes of traffic arriving from two arterial roads. It typically receives more than 50,000 vehicles a day, peaking at 95 cars per minute in rush hour. Figure~\ref{fig:UDOT} provides a snapshot of the simulated intersection (right) and a picture from the actual intersection (left). This intersection is affiliated with 10 phases, the 8 reported in Figure~\ref{fig:phases}, and 2 North and South bound permissive left turns. The minimum phase length is set to 3 seconds for this intersection. These 10 phases form 11 unique pairs of non--conflicting phase combinations. As a result, the affiliated designed control policy has 352 tunable parameters. The UDOT database (Signal \#7157) specifies the affiliated clearance interval's activation and duration.

In order to examine various traffic conditions, demand from 3 different days is chosen for simulation, Wednesday -- May 1, Monday -- May 6, and Friday -- June 21, all of 2019. For each day, a 14 hour time period is considered from 7 A.M. to 9 P.M. These dates were chosen as representative examples of low (May 6th), medium (May 1st), and high (June 21st) traffic volumes. Table 1 presents the total number of vehicles that crossed the intersection on each day as well as the average number of vehicles arriving per second, during off--peak (Low Rate), and peak--hour (High Rate).

The reported \textsc{cma--es} implementation is based on pycma~\cite{hansen2019pycma}, and the initial variance and population size were chosen to be 0.2 and 12 respectively. The simulated intersection controller is defined as an environment within OpenAI's GYM~\cite{1606.01540}, which gives a standard interface for reinforcement learning. Hyper--parameters for the online algorithms under all demand profiles aside from discount factor are identical throughout the different variants. The discount factor and epsilon were chosen empirically. Other values resulted in similar trends yet yielded slightly worse outcomes. Under low and medium demand the discount factor is set as 0.8. The discount is raised to 0.9 in high demands; this change was found to be important as clearing traffic from a set of lanes requires a larger planning horizon.

For DQN and the regulatable variants, the minibatch size is set to 32 and replay capacity at 100,000 transitions. The epsilon greedy action--selection probability in DQN and its variants is reduced from 0.05 to 0 (full exploit) after 20 training episodes resulting in the observed drop in the graphs. The Q--network is a DNN composed of 3 hidden layers with 64 units each, where the first layer is a 2x2 kernel convolutional layer grouping the input for lanes that belong to the same road. The Huber loss function is used in line with the original DQN work. Leaky ReLU activation is used for all layers along with the Adam optimizer with a step--size of 0.001, and decay rates $\beta_1$ and $\beta_2$ are set as 0.9 and 0.999 respectively. The same parameters were used for both optimizers in the two--stage regulatable variants. The PPO implementation follows the advantage actor--critic paradigm defining the actor by the regulatable function and the critic by a similar neural network as described prior with an alternative objective of estimating state advantages. The step size for each optimizer in this case are 0.001 for the actor and 0.001 for the critic with all other hyper parameters remaining the same.

Finally, as a baseline for comparison, the results include performance measures for the commonly deployed actuated signal control~\cite{bonneson2011traffic}. This type of controller is provided by the SUMO simulator. Phases for the actuated controller are set in a fixed order, protected lefts followed by through traffic. The maximum green time is set to 300 seconds.

\begin{figure*}%
    \centering
    \subfloat{\includegraphics[width=\textwidth]{Delays.png}}
    \caption{Average seconds of delay caused by the intersection under each demand profile. In each experiment the 95\% confidence intervals per episode over a population of 30 trials with random seeds is given.}%
    \label{fig:delays}%
\end{figure*}

\subsection{Regulatable control function}

The first set of experiments aim at addressing the question: can the designed control policy approximate a deep Q--learning optimized policy?

Given the stochastic nature of the signal domain and the combinatorial state space, it is  infeasible to compute the true optimal performance. Instead, we say that the control function can approximate an optimized policy if it results in performance that is competitive with a state--of--the--art, DNN based controller ~\cite{shabestary2018deep}. In order to address this question, a parameter tuning process is applied to the regulatable function. \textsc{cma--es} is chosen as the optimization approach since it is comparatively insensitive to hyper--parameter settings with just the population size and initial variance as hyper--parameters. Further, policy performance can be determined over entire episodes as \textsc{cma--es} is highly parallelizable. Finally, the simulator provides our goal of delay directly when vehicles exit the simulation. This allows for optimization of the designed control policy with respect to the delay rather than approximated through the observed waiting time as real--time algorithms require.

Figure~\ref{fig:delays} presents the average delay suffered by approaching vehicles for each of the three representative days as a function of the training episode (full day of traffic). For now, consider the DQN curve (representing the state--of--the--art, DNN based controller) and the \textsc{cma--es} datapoint (in green). As can be seen, \textsc{cma--es} with the designed regulatable control function achieves competitive delay measurements on all three traffic scenarios. Note that, \textsc{cma--es} is presented as a single data--point (post convergence), as it requires two orders of magnitude more samples in order to converge compared to the other approaches. The full tuning process cannot be fitted on the presented plots.

It is important to note that other, simpler regulatable control functions were also examined, specifically based on Polynomial~\cite{lagoudakis2003least}, and Fourier basis~\cite{konidaris2011value} function approximators. Both yielded extremely poor performance. After over 100 epochs either policy failed to complete the scenario by clearing all vehicles by the end of the simulated time period. The polynomial function showed some improvement, while Fourier basis was stagnant.

Despite its impressive ability to optimize the designed, regulatable function, \textsc{cma--es} is not practical for online optimization for two reasons. (1) Inefficient sampling -- each learning step (update of parameters' mean value) requires 24 episodes (24 full days of traffic). (2) Erratic exploration -- extreme parameters values are sampled leading to unacceptable performance during the tuning process.
These drawbacks are immediately evident in Figure \ref{fig:cmaes} where the average delay over each epoch is presented. \textsc{cma--es} fails to meet actuated performance for at least 25 epochs (600 episodes) for the medium demand scenario (similar trends were observed for the high and low demand scenarios). The erratic exploration is a hindrance as some solutions entirely fail to complete the scenario or perform much worse than average. Performance doesn't become stable for nearly another 4,000 episodes (11 years of traffic). Nonetheless, these results are still valuable as they provide a promising lower bound estimate on the performance of the regulatable policy (as seen in Figure~\ref{fig:delays}).

\begin{figure}[h]
    \includegraphics[width=0.9\columnwidth]{CMAESDelay.png}
    \caption{Average seconds of delay caused by the intersection under medium delay optimized by \textsc{cma--es} plotted as the mean of 24 episode epochs.}%
    \label{fig:cmaes}%
\end{figure}

\subsection{Policy gradient approach}

Policy--gradient approaches~\cite{williams1992simple} are known to be especially suitable for optimizing a given policy function while providing some bounds on the exploration rate~\cite{schulman2015trust,schulman2017proximal}. As a result, such algorithms are promising candidates for online tuning of regulatable functions in safety critical domains.

Figure~\ref{fig:delays} presents the performance curve for tuning the designed control policy using the PPO algorithm. As expected, the learning curve is smooth and monotonic suggesting a safe exploration process. On the other hand, PPO requires about 15 episodes to reach actuated control performance in low and medium demand, while not even reaching actuated level performance in the case of high demand. As a result, such an approach is unlikely to be adopted in practice. Moreover, bounded exploration can cause PPO to converge to suboptimal local optimums as evidenced by the presented results.

\subsection{Q--learning}

Given that the DQN algorithm achieves state--of--the--art results in the signal control domain (yellow curve in Figure~\ref{fig:delays}), the next set of experiments is set to examine how the regulatable Q--learning variants compare.

Figure~\ref{fig:delays} presents the performance curve for DRSQ (in purple) and DRHQ (in brown). As stated above, vanilla DRQ performs significantly worse and is, thus, not presented in this plot. Both DRSQ and DRHQ outperform PPO and, for low and medium demand, outperform actuated control after a single episode. On the other hand, it takes them 20 episodes to outperform actuated control for high demand (once epsilon is reduced to 0). In order to mitigate the long training time on high demand, Laval et al.~\citeyear{laval2019large} suggested to train the controller on low demand prior to applying it to high demand scenarios. Such an approach is expected to be specifically useful for training regulatable control functions.

DRHQ has a small, yet significant, advantage over DRSQ for low demand while yielding similar performance for medium and high demand. Finally, while DQN outperforms both, it is important to keep in mind that the policy induced by DQN cannot be interpreted and regulated. Consequently, DQN is unlikely to be implemented and serves only as an expected lower bound on regulatable performance.

\section{Summary and Conclusions}

This paper discusses online optimization of interpretable signal control policies. Unlike previous work that based such controllers on deep neural networks, this paper suggests utilizing a policy function that can be interpreted and regulated. A regulatable policy function is defined as one with a monotonic relation between each state variable and the precedence of a given action.
The following conclusions are drawn.

\noindent
\textbf{1.} A regulatable function can approximate an optimized policy in a way that is competitive with a deep neural network.

\noindent
\textbf{2.} A policy gradient approach is not suitable for training a regulatable function in this domain due to slow and sub--optimal convergence.

\noindent
\textbf{3.} A Q--learning approach which trains a regulatable function results in good performance both with regards to convergence speed and the final policy. The regulatable function should be trained to fit the hardmax action as provided by the underlying deep Q--network according to DRHQ.

Future work will examine techniques for warm starting the learning process by observing the operation of a currently deployed controller.

redpajama_set_name: RedPajamaArXiv


Graphic art Munch, Goya and Rops on view as of Spring 2017.
ANTWERP – The opening of the new Museum De Reede in Antwerp will be on the 10th of June.
The museum will also exhibit works loaned from private collections. The collection is made up of works on paper: lithographs, engravings, etchings, woodcuts and drawings from more than a dozen artists from seven countries on three continents, and spans over five centuries, from the 17 th until the 21 st. The core of the collection is some 200 works by the three of the greatest engravers in history: Francisco Goya (1746-1828), Félicien Rops (1833-1898) and Edvard Munch (1863-1944). It is the first time that such an extensive collection by these three artists has been exhibited in one space. For this reason the new museum expects a large amount of visitors from Belgium and abroad.


text:
Mislocked
BOJ policymakers warned of Japan's recovery delay, China risk
October 8, 2021 October 7, 2021 uufesorg
Some Bank of Japan policymakers warned of the risk of a delay in the country's economic recovery as state of emergency curbs to combat the coronavirus pandemic weighed on consumption, minutes of their July meeting showed on Tuesday.
While the nine-member board agreed that robust exports and capital expenditure would underpin growth, some also called for more vigilance to overseas risks such as the fallout from a possible slowdown in China's economy, the minutes showed.
"A few members said the timing of a full-fledged recovery in Japan's economy was likely to be somewhat delayed" compared with their projections in April, the minutes showed.
"Many members said the overseas economic outlook was highly uncertain with various risks," the minutes showed, citing one member as saying the possibility of China's economy decelerating "should be born in mind."
"If the rise in U.S. long-term interest rates accelerated, we must be vigilant to the risk of capital outflows from emerging economies," another member was quoted as saying.
The remarks shed light on policymakers' concern over the fragile state of Japan's recovery, even as they maintained the view the world's third-largest economy was headed for a moderate rebound from last year's pandemic-induced doldrums.
At the July 15-16 meeting, the BOJ kept monetary policy steady but cut this year's growth forecast from April as state of emergency curbs to combat the pandemic hit consumption.
Slow vaccinations and a spike in Delta variant cases have forced Japan to extend the curbs through September, accumulating the strain for some businesses.
The government is set decide later on Tuesday to lift all the curbs from next month due to recent declines in new cases, which may give the economy a much-needed boost from pent-up demand.
Logitech's new MX Keys Mini is a wireless, space-saving keyboard that costs $100
Study: Cancer centers provide superior end-of-life care to dual-eligible cancer patients
Endeavor to buy sports betting unit from Scientific Games for $1.2 bln
October 10, 2021 October 7, 2021 uufesorg
Boeing showcases eco-friendly tech as industry faces pressure
BlueBay, Blackrock, Ashmore and UBS exposed to Evergrande
October 7, 2021 uufesorg
Oisin Murphy thrown by colt in parade ring but will be fit for Arc
Tracking key protein helps predict outcomes in TBI patients
Netflix does not plan to buy a movie theater chain, co-CEO says
Battlefield 2042 open beta starts October 6 for select users
5 Midwestern Governors Sign Up to Create EV Charging Network to Keep Electric Vehicles Moving

redpajama_set_name: RedPajamaCommonCrawl


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What's inside REAL vs FAKE Rolex?What's Inside?


text:
Feature: The New Abolitionists The New Abolitionists...
The New Abolitionists
Operation Underground Railroad, a small Mormon-led group, is going undercover to rescue kids from sex trafficking. But is its brand of salvation working?
By Thomas Stackpole
Photos by Jared P. Moossy
On a warm morning in March, an American man named Paul stands on the balcony of a sprawling stucco mansion in Acapulco, Mexico. In the distance, the spring sun glimmers on the city's harbor, nestled among iconic white beaches and lush peaks. Acapulco is quiet—relatively speaking anyway. Caught in the cross-hairs of the country's gruesome drug war, a city that once bustled with cruise ships and spring-breakers now has the ignominious distinction of being Mexico's murder capital: 590 people were killed there in 2014.
The co-founder of a multibillion-dollar real estate investment fund, Paul is on the cusp of middle age. His short, graying hair is thick with gel, and he wears a pale blue shirt, sunglasses, and a Bentley-edition Breitling watch. He has come south of the border to take advantage of Acapulco's seedy underbelly. He isn't after property or drugs, however. He's looking for sex with underage girls.
Down below, a dozen other gringos are scattered around the mansion's pristine infinity pool. A mix of associates and Paul's imposing security detail—hulking ex-military types in Oakley shades—they sip beers and chew on cigars. On the balcony's railing, Paul carefully props an iPhone against a wine bottle so that he can look at the live visage of a friend in Silicon Valley, beamed in on FaceTime to watch the lurid show. "I have to apologize," Paul says. "There are only two girls coming."
A little after midday, the girls arrive. They have long, dark hair and are squeezed into strapless dresses. Leading them into the backyard is Mario, their squat, grim pimp. Men around the pool shout, "Hey, hey, Super Ma-ri-ooo!"
The girls greet the gringos with cheek kisses and totter in stilettos into an airy living room next to the lawn. There, a thick-armed security guard with a drooping blond mustache introduces himself as Brian and expresses some concerns to the pimp: Paul and his entourage got some young girls a few months ago, Brian explains, but when the boss started touching them, they flipped out. "If it happens again, I'm fired," he says. "So are these girls going to do everything?" Anal sex, Mario answers, "depends on how big" Paul is. But he insists the girls are game. Brian turns to one of them and asks how old she is. "Voy a cumplir diez y seis," she replies—almost 16.
At a wrought-iron patio table, the final details are hashed out as Paul and the Silicon Valley voyeur watch from above: $1,000 for the girls—half up front, half after the sex—plus a tip for Mario's troubles. "You're just like us," Brian tells the pimp. "You're not afraid to get a little dirty from time to time."
The girls greet the gringos with cheek kisses and totter in stilettos into an airy living room next to the lawn.
When two flashbangs explode in the street outside the mansion, the pops echo dully around the pool. For a moment, no one really seems to notice. But then more than two dozen police officers in black SWAT gear come pouring into the yard. "Abajo! Get down!" they yell, their assault rifles raised. A second column swarms in from a side entrance just under Paul's nose, their faces hidden behind balaclavas.
"You're working with the cops, are ya, Mario?" Brian yells as he drops to the ground. "You're fired. You're all fired!"
Stone-faced, Mario slips out of his chair and slithers belly down. A policeman grabs his arm and drags him into the middle of the grass before searching him. The teenage girls, now lying on the floor of the living room where they'd been left, put their hands over their heads. One begins to cry quietly. A female social worker co-
operating with the cops arrives at their side, cooing that they're not in trouble.
Their grotesque fun over, the Americans are led uncuffed into a ground-floor room of the house. Plucked from his perch, Paul is among the last hauled inside. "So this is where they're going to interrogate us?" he asks.
But it's a deadpan question. Paul smiles, and some of the other Americans laugh. The mood in the room quickly loosens. Everything in the sting, the men agree, went according to plan. "Oh, man, did you see Mario's face?" Brian asks. "These guys are going to jail for a long time."
Not a security guard at all, Brian is really named Tim Ballard. He's the founder of Operation Underground Railroad (OUR), a U.S.-based organization that goes undercover to rescue children forced into the sex trade. The Acapulco trip was the group's first foray into Mexico. In total, three people, including Mario, were arrested; they face prison sentences of up to 25 years, according to OUR.
Paul, a member of the OUR team who keeps his real identity private to protect his cover, holds up his phone: "It was awesome," proclaims the disembodied voice from Silicon Valley. An executive at a major technology firm—OUR won't provide his name—the man on FaceTime had donated the money needed to set up the operation. "Let's fund another," he says.
"This is going to end—and I'm not lying—in the rescue of thousands and thousands," Ballard rhapsodizes, still wearing his fake blond mustache. Without it, he's the epitome of the all-American man: tanned and fit, with bright blue eyes. "[The Mexican police] just learned how to do something."
After Mario and the girls have been removed from the mansion, the Americans pile into police trucks queued up to take them to Acapulco's airport. A loaned private plane is waiting on the runway. Ballard and Paul are due at a dinner in León hosted by former Mexican President Vicente Fox, and they're already running late.
Police raid an OUR party in the Dominican Republic.
Human trafficking is one of the world's fastest-growing criminal enterprises, according to the United Nations. Precise figures are hard to come by, given the inherent challenges of collecting data on illegal activity. But according to estimates from the International Labour Organization (ILO), trafficking is a $150 billion industry affecting 20.9 million people worldwide, nearly a quarter of whom are marketed for sex.
The ILO estimates that 5.5 million children are victims of the trafficking industry, and many are sexually exploited. Some young people are held or live in brothels, while others are forced into the hands of international criminal rings; still more are marketed by relatives seeking cash. What typically unites their stories is poverty. Pimps or networks of traffickers usually target people who are "poor, isolated and weak," according to a report by the U.N. Office on Drugs and Crime.
Efforts to eliminate sex trafficking have enjoyed prominent backing in the United States for about 20 years, ever since strange bedfellows—feminists who opposed sex work, politicians from both political parties, and right-wing Christians—rallied behind the cause of defeating modern-day slavery. In 2003, three years after Congress passed the Victims of Trafficking and Violence Protection Act, which established new laws against trafficking and rights for victims, President George W. Bush called sex trafficking a "special evil" in an address to the U.N. General Assembly.
Responding to the call for a moral crusade, a handful of private organizations has adopted what is now widely known as a raid-and-rescue strategy: identify where people are being sold for sex, send in police to haul them out, and arrest traffickers. Among the groups using this method is the International Justice Mission (IJM), a Washington, D.C.-based Christian legal organization with a presence in 11 developing countries; it claims to have rescued at least 258 people from sex trafficking and abuse in 2014 alone. The FBI uses the same model and says its busts have saved more than 3,600 trafficked children since 2003.
OUR is a new entrant in this field. Ballard was a U.S. government agent for a dozen years, including a stint at the Department of Homeland Security (DHS), for which he posed as a pedophile to infiltrate child-trafficking rings. But he became frustrated with red tape. While working abroad, Ballard says, "I could find children who were being sold into the sex trade, but if there was no U.S. nexus"—if the case would never land in a U.S. courtroom for jurisdictional or other reasons—"I couldn't pursue it." So in 2013 he struck out on his own and formed OUR, a small group of independent operatives who could set up stings anywhere in the world.
Ballard's Mormon faith also heavily influences his work. "The other option was to face my maker one day and tell him why I didn't do it," he says of his decision to start combating crimes against children. Ballard insists that religious belief isn't a requirement to join OUR but notes that the staff members often pray together. If someone isn't "comfortable praying," he says, "they're not going to be comfortable working with us." (In a February interview with LDS Living magazine, Ballard was more candid about his faith: He said he launched OUR after being instructed by God to "find the lost children.")
Responding to the call for a moral crusade, a handful of private organizations have adopted what is now widely known as a raid-and-rescue strategy: identify where people are being sold for sex, send in police to haul them out, and arrest traffickers.
Today, OUR has a full-time staff of 12 people and a stable of trained volunteers, most of them Mormon. They include former military and intelligence officers, nurses and Army medics, cops and martial arts instructors. From small offices in Salt Lake City, Dallas, and Anaheim, California, OUR has coordinated more than a dozen raids in Latin America and the Caribbean. It claims to have saved at least 250 trafficking victims, including 123—55 of whom were children— in three stings coordinated across Colombia last October.
Simultaneously, OUR is making a public splash by amplifying the drama of its tactics and the ways people can support the group's cause without ever busting into a brothel. A documentary movie, called The Abolitionists, has been screened privately in select U.S. theaters, and a proposed TV series about OUR is currently being filmed. The organization's "give a Lincoln, save a slave" campaign, which like the term "underground railroad" conjures noble notions of 1800s anti-slavery efforts, asks people to become "abolitionists" by giving $5 a month. Supporters can sign up to receive text-message alerts "when children are saved." If they're big funders, they can get front-row seats: The tech executive watching the Acapulco operation gave more than $40,000.
As of this writing, OUR has 229,000 likes on its Facebook page, 3,000 more than the veteran IJM has. According to Jerry Gowen, OUR's chief operating officer, the organization has raised almost $5 million since its founding less than two years ago. Celebrities, many of whom are Mormon, are getting on board too. The Walking Dead star Laurie Holden and Dancing With the Stars' Chelsie Hightower have participated in raids. Utah Attorney General Sean Reyes went undercover with the group. This March, OUR announced its merger with the Elizabeth Smart Foundation, a child-protection NGO run by the family of the young Mormon woman famously kidnapped in Utah when she was just 14 and held in captivity for nine months.
OUR and its growing network of backers are nothing if not committed and well intentioned. But do their chosen methods actually work? The answer is anything but clear-cut.
Though most people can get behind fighting human trafficking, how to wage the war is another matter. Nor is claiming victory necessarily quick or simple. After a raid, there's long-term support to consider, such as psychological care and rehabilitation for victims; this could take months, if not years. "To realize success in a lot of these cases takes a lot of time," notes Rebecca Surtees, a senior researcher at the Nexus Institute, an international human rights research and policy organization.
But time, OUR argues, is exactly what children being sold for sex do not have. Getting them out of a horrendous situation as fast as possible is the top priority. "The children are desperately waiting for us," Ballard testified before Congress in May, advocating that the U.S. government do more to combat trafficking. "I know. I have seen them."
Right, wrong, or flawed, this urgent mission only seems to be gaining steam. Between February and April, OUR staged five operations in as many countries, including its first in Thailand. "This idea of actually doing something is very powerful," says Anne Gallagher, an expert in trafficking and an advisor to the United Nations. "It's addictive to people."
The night before the raid in the Dominican Republic, Dutch Turley points at a ledger that documents how many girls each trafficker plans to bring to the party (left); Turley, a former Navy SEAL, does crossfit (right).
Four days after the Acapulco bust, Ballard is sitting on a plastic lawn chair on a beach in Sosúa, a town on the Dominican Republic's north shore. It's late morning, and behind him is a strip of tourist restaurants and tchotchke shops. On another chair nearby, Dutch Turley, a 6-foot-3-inch, 230-pound former Navy SEAL, is getting a $10 pedicure from a woman with dyed red hair who carries a small nail kit in a bucket up and down the beach.
The lazy scene belies an early step in OUR's next raid: The men are waiting for two young Dominican traffickers who the day before had promised they could deliver girls, maybe even some as young as 12.
When the men arrive, they're wearing board shorts; one sports a Lakers hat. Standing near the Caribbean surf, they tell Ballard and Turley that they have pictures of the teenagers on offer. "I know you guys are tourists," one says, "but you can't have cameras." It's too risky to let evidence leave the scene. In the end, they promise to bring 13 girls the next day to a party—the cover for the operation.
The Sosúa sting is following OUR's usual pattern. The first phase is finding a government, in a country with high trafficking rates, willing to cooperate with the group. OUR's staff members reach out to people they know from their former lives as agents and soldiers: local police and prosecutors with whom they're already friendly or representatives from the State Department, FBI, or DHS who know the territory. In the Dominican Republic, the group secured a memorandum of understanding with federal police before getting to work.
That work is done by what OUR calls its "jump team." Ballard coordinates trips and inhabits fake identities as needed. Paul plays the moneyman; lest anyone question him, he has created a false, elaborate identity online, complete with a Facebook profile boasting pictures of yachts and private jets to advertise his lavish playboy life. Turley handles tactical details—who goes where and when during raids—and can act as muscle if necessary. Matt Osborne, OUR's senior vice president for rescue and rehabilitation, acts as the main liaison with local law enforcement. Then there's Krista Rykert, a tall, blond CrossFit instructor and gym owner from a Salt Lake City suburb who plays the "groomer": She talks to the children as a sting is happening, gives them candy, plays games—whatever is needed to keep them distracted. The film crew for the TV series is in tow as well; cameramen shoot the jump team using lenses hidden in backpacks, water bottles, and sunglasses.
OUR operatives walk the streets of whichever city or town is their latest target and pose as potential sex customers. They go to bars, talk to hustlers, explain that they're throwing a party and want to cut a deal that will satisfy their boss's desires. Sometimes Paul himself goes looking for traffickers; he throws money around, buys strangers drinks, and telegraphs that he wants particularly exotic partners—meaning, underage girls. (The group is careful not to entrap potential targets.)
In Sosúa, the jump team has trolled beaches and the local red-light district, thick with frumpy Americans and Europeans in town for sex. "Some guy will almost always come up to you and ask you if you want something," Ballard says. "'You looking for some smoke? Maybe a girl?'" One woman at a roadside restaurant even offered her daughter, who she claimed was 17, and five of her friends. (The age of consent in the Dominican Republic is 18.) Wearing tank tops and heavy makeup, the girls smiled at Osborne as he pretended to check them out.
The goal is to get as many children as possible to the site of a bust. By the night before OUR's party in Sosúa, seven people, including the young men from the beach, have said they can bring more than 26 girls for Paul and his friends.
OUR has rented two houses—one for the faux celebration, the other across the street as a hideout for cops. Both are modern, all stone and glass, and sit in a tony, gated community a short drive from the beach. The documentary crew carefully places more than 20 cameras throughout the party house. (Police often use this footage as legal evidence.) Some $7,000 in cash is meticulously laid out on a bed and photographed before being divided into envelopes for each trafficker.
For final preparations, police officers and members of the local prosecutor's office stop by. The logistics are explained: Some police officers will come in through the driveway, while others will enter a side door by the kitchen. The cues for storming will be a text message from an undercover Dominican cop working alongside OUR when Ballard shouts the words, "Bring in the wine!"
The prosecutor, who will ultimately try the case against the people arrested, is satisfied. "Remind everyone to keep straight faces," admonishes a contact from the U.S. Embassy who has come to survey the setup. The mission is a go.
Tim Ballard speaks on the phone at the sting house in the Dominican Republic (left); cameras are used to document OUR raids (below).
OUR says its method of collaborating with law enforcement and luring traffickers works like a charm. At a $200-per-plate gala in Washington, D.C., last November, Ballard regaled more than 260 guests with success stories. Wearing a dark suit and a slightly too wide red necktie, he told the crowd at the JW Marriott, "I don't care about borders and boundaries when they're kids." A teaser for The Abolitionists played. OUR makes slam-dunk cases, Ballard's voice-over explained, and then ensures they're "delivered to [law enforcement] on a silver platter." The gala raised more than $150,000.
Critics, however, are quick to pick apart claims of triumph, as they have been since the advent of raid and rescue. IJM largely pioneered the field in the early 2000s when it conducted high-profile stings across Southeast Asia; during a March 2003 bust in Cambodia, journalists from Dateline tagged along to produce a widely watched segment called "Children for Sale." Later, in 2011, IJM took New York Times' columnist Nicholas Kristof along for an operation in India. IJM's approach quickly gained acolytes. An Internet search reveals numerous raid-and-rescue groups with names like Destiny Rescue and The Exodus Road. "The undercover and mass-mediated model of activism that IJM propounds has become the emulated standard," Barnard College professor Elizabeth Bernstein, a prominent critic of raids, has written.
Detractors, including many health and human rights advocates, argue that stings are only as good as their ability to actually improve lives—and that they often do the opposite. "The appeal of the rescue is that it's a happy ending," says Janie Chuang, who teaches courses on trafficking at American University's Washington College of Law. "But it's not. It's a really hard life."
In some cases, victims are quickly cut loose because governments lack the resources or concern to assist them. Others choose to leave protective services; sometimes they fear that authorities will abuse them or that traffickers will do the same to their families. (This is to say nothing of rescued adults who weren't trafficked at all but had chosen to be sex workers, a distinction that raid groups often fail to make.) Mother Jones found in 2003 that girls and women saved in an IJM bust in Thailand were "locked into two rooms of an orphanage by Public Welfare authorities" and were allowed outside for only one hour each day. Following up on the operation featured on Dateline, the Nation reported in 2009 that some of the rescued children were addicted to intravenous drugs and made deals with the police to keep using; at least a dozen ran away and returned to brothels. "You hear about the raid, but you don't hear a lot about the safe houses, the rehab process," says Gretchen Soderlund, a professor at the University of Oregon who studies trafficking.
Sometimes, the consequences can be even worse. In the same investigation, the Nation learned that IJM didn't track minors rescued in Thailand, including young girls from Myanmar who subsequently may have been deported back to their oppressive homeland. It also found that busts in Cambodia disrupted health NGOs' efforts to educate women and girls in brothels about HIV; pimps believed the groups had aided IJM and no longer wanted them providing care.
Holly Burkhalter, IJM's vice president of government relations and advocacy, shot back in 2012 in the Anti-Trafficking Review, "This view suggests that there is some level of backlash by brothel owners against health workers that would justify leaving the children to their fate." Critics, she added, "have not offered any alternative to police operations to apprehend perpetrators and bring them to justice. That is because there are none."
Still, IJM has tweaked its approach over time. "It's not just a … drop-in to get a couple of children out of a brothel and then leave. We did that in the early days," Burkhalter said in an interview with Foreign Policy. IJM now sets up offices in countries where it works—it recently opened one in the Dominican Republic—and places greater emphasis on training police and building the capacity of judicial and social-service systems. "We want to walk away from the image of the Western superhero going into places of darkness to rescue … the little girl," says Pablo Villeda, IJM's vice president of regional operations for Latin America.
"It's not just a ... drop-in to get a couple of children out of a brothel and then leave."
Ballard knows the criticisms that have plagued other raid-and-rescue outfits, and he is wary of OUR being characterized as a group of vigilantes. He insists that his organization has strong relationships with its police partners and that its missions are intended to set examples for future stings. OUR is also developing software that could flag international travelers whose computers are known to have downloaded child pornography—a tool that could help foreign officials intervene before customers even get to traffickers.
Still, the organization has opened itself to plenty of reproaches. Busts, Soderlund says, are "very strategic events that are almost tailor-made for the media." OUR has embraced this notion, using the Internet, television, and film to push a slave-to-saved narrative. But Chuang says this story is an oversimplification that "just seems to be glorifying the savior." She also worries that flashy campaigns divert donor funding from "the mundane work that needs to be done on the prevention side" of the trafficking equation—a concern shared by Randy Newcomb, president and CEO of the San Francisco-based philanthropy Humanity United, who wrote in the Anti-Trafficking Review in 2014 that donors' desire for visible results has had "the unintended consequence of growing the capacity of only a select group of organisations that may, in fact, be more successful at marketing and far less successful at actually ending trafficking."
Unlike IJM, OUR doesn't have plans to shift from its parachute approach. "We really feel like we're not in the building-
homes business," says Gowen, OUR's chief operating officer, referring to planting roots in foreign locales. "That's not our … core competency." This isn't to say that the group isn't concerned with aftercare: OUR routinely links up with local entities that can assist the children gathered during raids and says it is hoping, with resources from the Elizabeth Smart Foundation, to provide these groups with a best-practices guide and funding.
This model, however, doesn't always work. In 2014, after OUR's first operation in the Dominican Republic, a local organization called the National Council for Children and Adolescents (CONANI when abbreviated in Spanish) quickly discovered it didn't have the capacity to handle the 26 girls rescued. They were released in less than a week. Some still went on to testify against the men arrested in the sting—as of press time, a verdict had yet to be delivered in the case—but CONANI lost track of others. "The influx of a large number of victims at once is very challenging to the social-service side," says Fernando Rodriguez, IJM's field office director in the Dominican Republic. (IJM has coordinated with OUR on two raids.) "To some degree, it is potentially a disservice and creates more problems than it would solve."
Sometimes, OUR takes matters into its own hands. After the Acapulco bust, which was much smaller than anticipated—and one of the two girls saved turned out not to be a minor—OUR decided to take care of the almost-16-year-old's financial needs. She was placed at a shelter in Mexico City and "wants to be a beautician," Osborne says, estimating that OUR will provide $20,000 raised over the next few years for her care and education. "In the small rescues you don't get as many," he explains, "but you can really, really make a difference in the life of this girl."
Dominican police arrest alleged traffickers and OUR members during a raid.
On the day of the Dominican raid, a bevy of teenage girls arrives in a caravan of vans, shuttle buses, and SUVs at the house rented for the fake party. Some have come from as far as Santo Domingo, the Dominican Republic's capital; the city sits 125 miles away, on the country's opposite coast. Wearing colorful dresses, the girls stand around the backyard pool, chatting nervously. At one point, all of them start singing. Rykert, the OUR groomer, has told them it's her birthday—a way to keep the girls busy as, behind sliding glass doors, other operatives negotiate the day's deal. To further the lie, the deck has been decorated with pink and yellow balloons, and the gringos saunter around drinking Red Bull poured into Presidente beer bottles.
Rykert towers over the teenagers, her wrestler-size arms stretching out of a cobalt-blue tank top. She hams it up, conducting the singing with her hands: "Cumpleaños feliz!" The chorus peaks in an off-key "Deseamos Mariaaaaa"—the fake name Rykert is using—"cumpleaños feliz!" The girls, gathered in an arc, burst into applause.
As the teenagers and Rykert take selfies, Ballard, Turley, Osborne, and undercover Dominican police hand the traffickers the cash. "Vino!" Ballard yells to his associates, as one of the cops shoots off the text-message signal.
What happens next is much the same as in Acapulco. The Americans pretend to be shocked as the cops rush in. The teenagers begin to cry. The traffickers, who had been grinning at their good luck, turn dumbstruck. Afterward, the Americans and the police congratulate each other, but the celebration is once again short-lived: Ballard has to get to the airport to make a meeting back in the United States. "Can we get a quick wrap-up?" asks one of the cameramen as OUR's founder grabs his bags. "Some of [the girls] were crying on the way here," Ballard says to a camera before pulling off his hidden wire. "These were truly, truly kids being trafficked."
A few members of the OUR team stay behind, planning to lay low for the night at one of the tawdry all-inclusive resorts that dot the Dominican Republic. They drive away from the house not long after Ballard, in the vehicles that brought the girls to the house. The teenagers have all been taken to CONANI and been given access to IJM psychologists. A few hours after the raid, OUR's Twitter feed boasts:
UNOFFICIAL NUMBERS:
29 SAVED
6 ARRESTED
YOUNGEST AGE: 13
Less than three weeks later, the girls are released to their families on a judge's order—well short of the three months of targeted care the rehabilitation organizations had hoped to provide. IJM's Villeda claimed in an interview with Foreign Policy that his group asked OUR to consider a smaller operation "knowing that the Dominican government didn't have the capacity to house the number of victims that they were expecting to rescue." OUR, however, insists it was the government's call. "Were there too many that were brought? Perhaps," Ballard said in a phone interview in June. "But that's the number that the Dominicans wanted."
He also detailed his plans for his group's future. "It's not just a bunch of sex parties," he explained. "It's going to be raids on brothels; it's going to be buying one kid on the beach from one trafficker … [and] military-
style raids on a slave-labor camp." OUR, in other words, is just getting started.
Thomas Stackpole (@tom_stackpole) is an assistant editor at Foreign Policy. He embedded with OUR operations in Mexico and the Dominican Republic.


text:
Argonne's Zaluzec Becomes Microanalysis Society Fellow
Nestor J. Zaluzec, a senior scientist at the U.S. Department of Energy's Argonne National Laboratory, has been named an inaugural fellow in the Legends Class of the Microanalysis Society, an award that recognizes his contributions to advanced microscopy and microanalysis.
Over the course of his ongoing career at Argonne, Zaluzec has pioneered advanced techniques and instrumentation for state-of-the-art electron microscopy. The Microanalysis Society recognized Zaluzec for his outstanding leadership and sustained contributions to advanced analytical electron microscopy, including development of scanning confocal electron microscopy and his work on combining spectrometry with electron diffraction.
"In order to understand and actually move forward in today's technologically demanding society, what we have to do is more than simply look at a material's structure; rather, we have to fully characterize it," Zaluzec said. "We have to understand the relationship between morphology; crystallography; and elemental, chemical, and electronic structure, and we have to integrate this information so that we can unravel the synergistic relationship of a material's macroscopic properties to its atomic structure."
The future of microanalysis, he said, lies in what scientists refer to as "multimodal and multidimensional" techniques, as well as new and novel detectors — which can be combined in different ways to study a material. At Argonne, combining x-ray microscopy, optical microscopy, and electron microscopy provides one example of this approach. Through this combination, Argonne enables scientists to capture a comprehensive range of information about the constituent phases and individual atoms within a sample.
"In the 1960s, we were working on the basics, operating at the μm scale — we were merely dreaming of the ability to achieve single-atom resolution," Zaluzec said. "Now, we have the capability to not only resolve but also analyze materials at the picometer scale, and the view we have today at the atomic scale is fascinating. One analytical technique or one analytical instrument is no longer enough to answer all of today's questions. We need correlative approaches that integrate and amalgamate our experimental studies in order to unravel and comprehend the complex interactions found in modern systems."
The nation's first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state, and municipal agencies to help them solve their specific problems and advance U.S. scientific leadership.
UC Riverside's Professor Receives $750K DOE Grant for Organic-Inorganic Materials
Larson Named MSA Fellow
Kevan Named ALS Director of Berkeley Lab
CSU Professor Earns Webb-Waring Early Career Award
Tuned Resonators Allow Control of Electromagnetically Induced Transparency
Diabetic Smart Contact Lenses Developed by South Korean Research Team
Deep Learning Speeds 3D Microscopic Neuroimaging
BusinessNestor ZaluzecU.S. Department of EnergyArgonne LaboratoryMicroanalysis SocietyMicroscopypeopleAmericasBiophotonicsRapidScan
Thin-Film Coatings
Infrared Imaging Systems
Infrared Lenses
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CANON MEDICAL, Video Sensing Div. (VSD)
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Superresolution Microscopy Poster
Visually stunning poster illustrating methods and fundamental concepts The diffraction limit...
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text:
Q: SUMIFS in DAX Calculated Column SUMIFS in DAX Calculated Column

*

*I am looking to create a DAX calculated column that will recreate the SUMIFS formula in column F in the table below (built in Excel).

*In the Excel table the Allocation column (column F) is a SUMIFS that gives me the Monthly (Column D) Qty (Column E) as a percentage of the annual qty by Customer Type, Family, and MPF (Columns A, B, C).

*This is the SUMIFS formula from Excel to build Allocation column: =E2/SUMIFS($E$2:$E$25,$A$2:$A$25,A2,$B$2:$B$25,B2,$C$2:$C$25,C2)

*I've looked at other solutions for similar questions and can't find one that is relatable enough for what I'm looking to solve.

CustomerType
Family
MPF
Date
Qty
Allocation

SHR
Family1
MPF1
1-Jan-22
        18,668
7%

SHR
Family1
MPF1
1-Feb-22
        12,917
5%

SHR
Family1
MPF1
1-Mar-22
        20,150
8%

SHR
Family1
MPF1
1-Apr-22
        20,447
8%

SHR
Family1
MPF1
1-May-22
        26,591
10%

SHR
Family1
MPF1
1-Jun-22
        26,482
10%

SHR
Family1
MPF1
1-Jul-22
        25,398
10%

SHR
Family1
MPF1
1-Aug-22
        26,718
10%

SHR
Family1
MPF1
1-Sep-22
        23,275
9%

SHR
Family1
MPF1
1-Oct-22
        17,357
7%

SHR
Family1
MPF1
1-Nov-22
        22,360
9%

SHR
Family1
MPF1
1-Dec-22
        15,438
6%

SUB
Family2
MPF2
1-Jan-22
        15,407
7%

SUB
Family2
MPF2
1-Feb-22
        14,486
6%

SUB
Family2
MPF2
1-Mar-22
        19,888
9%

SUB
Family2
MPF2
1-Apr-22
        19,238
9%

SUB
Family2
MPF2
1-May-22
        21,765
10%

SUB
Family2
MPF2
1-Jun-22
        20,367
9%

SUB
Family2
MPF2
1-Jul-22
        18,356
8%

SUB
Family2
MPF2
1-Aug-22
        23,161
10%

SUB
Family2
MPF2
1-Sep-22
        19,623
9%

SUB
Family2
MPF2
1-Oct-22
        17,559
8%

SUB
Family2
MPF2
1-Nov-22
        18,330
8%

SUB
Family2
MPF2
1-Dec-22
        16,175
7%

A: I figured it out, although I'm open to better solutions! I'm not convinced the numerator variable is as good as it gets. Thanks everyone!
QtyAlloc = VAR Numerator = IF(['Table'Qty] > 0, 'Table'[Qty], 0)
VAR Denominator = CALCULATE(
  SUM('Table'[Qty]),
  ALLEXCEPT('Table',
    'Table'[CustomerType],
    'Table'[Family],
    'Table'[MPF]
  )
)
RETURN DIVIDE(Numerator, Denominator)

redpajama_set_name: RedPajamaStackExchange


This task creates a graph file that can be passed to GraphViz to generate a project dependency graph of the projects registered with WeaverProjects.
This task will require GraphViz to be installed, and the GraphViz 'bin' directory to be added to the 'PATH' system environment variable.
Call 'WeaverGraph' task to create a 'projects.dot' GraphViz input file. Call GraphViz executable, 'dot.exe', to receive 'projects.dot' and generate 'projects.jpeg'. This example assumes that GraphViz is installed at the specified directory.


Often referred to as the pedal boats or paddle boats, the Water Skeeters were the last in a long history of attractions on Lagoon Lake. During the early days of Lagoon around the turn of the 20th century, the lake was about four times larger than it is today. Boats of different shapes and sizes offered Lagoon guests a moment of relaxation on the tranquil man-made lagoon which the park was named for.
When the Water Skeeters opened to guests in spring 1971, there were 20 boats carrying two passengers each. A path was marked by buoys on the southwest side of the lake. In that first year, the lake was shared by a small riverboat called the Showboat. After the Showboat was taken out of operation, floating guide rails designated the path for riders to follow which traveled around the entire lake. The dock was in the northwest corner of the lake. When Tidal Wave opened in that location in 1980, a new dock was built in the northeast corner accessed by a bridge that crossed over the path of the boats. The large metal gate used for the Water Skeeters remained in place at the entrance to Tidal Wave until the early '90s.
At least three different types of pedal boats were used during the ride's 21-year lifespan. The original multi-colored boats were traded out for similar boats of a different style before being replaced by metal boats. The ride was removed completely after the 1991 season.


text:
Securing Freedom to Eat
Guido Barilla, Chair of the Barilla Foundation, says the future of food is in our hands if we act to fix the problems in our food system. Credit: Busani Bafana/IPS
by Busani Bafana (bulawayo, zimbabwe)
BULAWAYO, Zimbabwe, Dec 01 (IPS) - For Zimbabwean organic farmer, Elizabeth Mpofu, access to healthy food is liberation.
Millions of people across the world go to bed hungry. Scores do not have access to nutritious food owing to an inequitable global food system focused on industrial mass food production. The food from this system is less nutritious, more expensive and less friendly to the environment.
How to achieve just, equitable food systems where more people do not only have enough to eat but have nutritious food was the central question food experts sought to answer at the one day 'Resetting the Food System from Farm to Fork'. The international dialogue was co-hosted by the Barilla Foundation and Food Tank and forms a critical part of the discussions ahead of the 2021 UN Food Systems Summit.
"We should not be talking about food security in the world today but about food sovereignty, if we are seeking to end hunger and malnutrition," Mpofu told IPS in a telephonic interview from her farm in Zimbabwe's Masvingo Province. It is here that where she grows drought-tolerant sorghum and finger millet, cowpeas, groundnuts on ten hectares of land.
"Food sovereignty is about giving farmers control over how they grow food, what food they grow, what seeds they use and how they consume that food because it is food grown in a sustainable way," said Mpofu. This is a subject close to her heart as she doubles as the general coordinator of the international peasant movement, La Via Campesina, which advocates for an agroecological approach to farming. The methodology promotes resilient and sustainable farming and food systems through agroecology, diversified health and nutritious food systems.
Farmer Elizabeth Mpofu on her maize plot. Courtesy: Elizabeth Mpofu
A sustainable food system
The world needs a new food system where all actors from farmers, civil society, researchers, chefs, policymakers and business leaders act together to create a more sustainable food future for all, was a crucial message in Tuesday's dialogue.
Guido Barilla, chairman of the Barilla Foundation, said the Covid-19 pandemic had shown how interconnected we all are with each other and the planet.
"This crisis is the latest example of the increasing pressure and expectations being put on the world's food system – not only to keep us all fed but to ensure we are all nourished and to do so while looking after the environment tackling the climate crisis and ensuring people's livelihoods continue to be met," Barilla said calling for a fundamental shift in attitude and making radical choices to build a transformative agenda for a sustainable and equitable future.
"I am not afraid of the change we need to make, the future of food is in our hands, said Barilla. "Let's make the future grow. And the list of potential improvements, from farm to fork, could be long and exciting."
Asma Khan, an Indian-born British chef and owner of the Darjeeling Express Restaurant in London, called for a shift in eating patterns of buying and eating less to cut food waste.
"COVID was a lesson that food systems are vulnerable and that we are all connected," Khan said urging that restaurateurs can promote food equity by respecting food and seeing food as an opportunity to make a difference.
"Let us not use food as our right … you don't have a right to eat as there are many people waiting to eat … Hunger is relentless," said Khan.
"It is important that we respect the fact that we have a privilege to eat. I really would want to honour the food we eat because there are many people who do not have this opportunity and we should be responsible. There is no justification to throw away food."
Khan's comments led to a discussion by farmers who outlined the
the myriad of challenges - from access to land, land grabs, poor skills, climate change and impact of COVID-19 that they face.
Speaking at a panel session on Farmers Feed the World, Leah Penniman, co-founder of Soul Fire Farm and author of Farming While Black, said land was a vital issue in empowering farmers in the United States. She called for reparations for indigenous communities and black communities who lost their land through expropriation.
"When we talk about reparations, we really need to look at land reform and distribution, and there are models such as the North East Farmers of Colour and the Black Family Land Trust which have ways of putting land into permanent protection so that it can be used for full developing agriculture," said Penniman. She noted that the US government realised the need to put land in trust to ensure that people dispossessed of land could have the chance to return to farming.
In Africa, land grabs have affected farmers and the agriculture sector.
Land grabs are some of the biggest injustices are farmers have faced, said Edie Mukiibi, vice president of Slow Food International. He advocated the building of a movement to ensure that farmers are recognised for their roles in food production and that agroecological approaches be prioritised.
James Maes, president of the European Council of Young Farmers (CEJA), agreed that farmers across the world faced similar challenges. However, contexts were different, he said, and there was a need to uphold the farmers' rights to produce food. A change of narrative was needed on food systems debates.
"I would question the need to reset food systems. I believe resetting comes at a huge economic and social cost for those already involved in that food system," Maes said, noting that the perspective should be on improving and empowering farmers to build resilience and to enhance their production.
As a farmer, Mpofu had a positive perspective on sustainable food systems:
"We have been talking about food security year in year out. We need to stop filling bellies and start promoting nutrition, and this lies in agroecological approaches."

Securing Freedom to Eat, Inter Press Service, Tuesday, December 01, 2020 (posted by Global Issues)

Securing Freedom to Eat, Inter Press Service , Tuesday, December 01, 2020 (posted by Global Issues)


text:
This day In Country Music History 1/17/18
Welcome to Wednesday January 17 2018
Today is National Bootleggers Day, and National Hot Buttered Rum Day,. (More information available at national day) Benjamin Franklin, was born on this day in 1706. Also U.S. mobster known as"Scarface Al Al Capone was born in 1899.
It Happened On This Day In Country Music History
1955 Happy Birthday Singer/songwriter Steve Earle born on this day in Fort Monroe, Virginia. Best known for reaching #7 on the charts in 1986 with "Guitar Town"
1971 Happy Birthday Bob Ritchie, better known as rap-rocker Kid Rock, born on this day in Dearborn, Michigan. He collects a country hit with "All Summer Long" and earns country awards nominations for his duet with Sheryl Crow, "Picture"
1981 Eddie Rabbitt's "I Love A Rainy Night" hits #1 on the Billboard country singles chart.
1982 Happy Birthday Amanda Wilkinson born on this day in Belleville, Ontario. With father Steve brother Tyler they form The Wilkinsons, best known for 1998 #1 single "26 Cents"
1998 Tim McGraw begins a six-week residency at #1 on the Billboard country chart with "Just To See You Smile"
2000 Norm McDonald presents the Artist of the Decade to Garth Brooks for the '90s during the American Music Awards at Los Angeles' Shrine Auditorium. He also wins trophies for Favorite Male Country Artist and Favorite Country Album.
2006 Sugarland posts a message on its website announcing Kristen Hall is leaving the trio to concentrate on songwriting.
More information on these and other stories available at CMT
Related Items:101.5 the eagle. Tracy Chapman, country music news, eddie rabbitt, garth brooks, kid rock, nashville birthdays, steve earl, sugarland, the wilkinsons, tim mcgraw
Garth Brooks Officially Pulls Himself Out of the CMA EOTY Category
Kid Rock's Bar Joins Lawsuit Over COVID-19 Restrictions
Garth Brooks Announces His Concert Will Be Shown At Drive-In Theaters
Meteor Over Michigan Footage is Incredible
I Usually Don't Post Or Comment About Such Things


We develop standard ("vanilla") and structured ("exotic") option contracts in line with customers' needs so that they may confidently conduct their business activities while simultaneously minimizing the risks they assume.
The price of an option is determined by five essential factors: the spot price of the underlying asset, the strike price (the price that triggers the option), the number of days remaining until the contract's maturity, the volatility in the underlying asset's price, and the theoretical "risk-free interest rate".
The risk incurred by someone buying an option contract is limited to the amount of the premium that he pays to the seller. For the seller, who also incurs principal risk on the contract, the risk is potentially unlimited.
For detailed information about the tax consequences of options trading in Turkey please click here.


Hotels near Sydney Convention and Exhibition Centre, Sydney, Australia .
The Sydney Convention and Exhibition Centre is a state-of-the-art facility located in a prime position on the thriving entertainment district of Darling Harbour. This purpose built facility offers a wide range of function halls, rooms, theatres and exhibition spaces. The centre has a fantastic collection of artwork, stunning waterfront views and good food and wine. There is a wide range of accommodation in the immediate vicinity.
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text:
MaryKate Cleary
Collection Specialist, Department of Painting and Sculpture
MaryKate C. Cleary works at the Museum of Modern Art, specializing in the collection of painting and sculpture and provenance research. Previously, she worked on issues of Nazi-era cultural property as Manager of Historic Claims and Research at the Art Loss Register in London, and held roles at Sotheby's, artnet.com and the Jewish Museum New York. MaryKate earned a BA in German Literature from Catholic University in Washington, D.C., an MA in History of Art from the Courtauld Institute London and was a Fulbright Fellow at the Technische Universität Dresden.
Ghosts of the Past: Nazi-Looted Art and Its Legacies