Over the past five years, we've had thirty-three of our students either end up in the hospital or getting professional care for suicidal actions.
So many approaching us personally. Others by their parents or friends. We'd always end up meeting or visiting with them. Sitting with them. Talking with them. Listening to them. Eating. Laughing. Crying. Reading scripture. Telling stories back and forth. We'd pray and I'd share a few words of encouragement. And then we'd leave.
And as we left as I always wondered, what could I leave them with besides just my presence? What about my gifts? Something they might be able to hold onto. To touch.
Recently, I wrote poems that were really just those words of encouragement I shared but in writing. They were for a single student who I helped every morning who struggled with similar things.
So I did only what made sense. Kept being present. Kept sharing scripture. Kept helping in whatever way. But also, I wrote some for these others too.
So I wrote them on notecards, napkins, some in a text. Anything I could find. I'd say a little prayer in my head and hand it off as I'd leave.
Each student reached out afterwards in ways I don't think they ever would have.
And then as I continued on in ministry, we discovered more. Those thirty-three students doesn't include the hundreds (yes, hundreds) of others that have suffered from related issues like depression, anxiety, and addiction.
Unfortunately, I've found that this is the new epidemic among young people. And it's hit our students quite a bit.
So many experiencing lows that were harder than anything I've personally experienced. Wondering why one day they feel ready to conquer the day when, for no reason at all, they then feel the darkest version of depressed and lonely. A daily roller coaster of emotions and spirit draining.
So I wrote many other words for these students as well.
And that, ultimately, led to this book.
The story of their life is not finished yet. And it's something to keep going after.
I'm not saying that these words and this book is THE solution for these students.
All I know is I felt led, once again, to at least do something about it.
And now hopefully these same words can help you in your story as well.
So that you can keep going.
For your story is written with redemption.
***To get your own copy now, GO HERE. Can't wait for you to read it!


text:
\section{Introduction}
\label{introduction}

Combinatorial optimization is the science that studies finding the optimal solution from a finite set of discrete possibilities. Historically, combinatorial problems have been approached with exact algorithms, which guarantee the optimality of the solution. Conversely, they are not guaranteed to do it in polynomial time, and thus, when large problems are optimized, exact methods are no longer a feasible option. Today, many of the combinatorial problems are classified, according to the theory of complexity, as NP-Hard~\cite{gary1979computers}. For problems that fall in this category, it is intractable to achieve the optimal solution systematically, as an algorithm to solve them in polynomial time has not been discovered yet.
\\[3pt]
In those cases, approximation methods such as handcrafted heuristics are used to obtain near-optimal solutions. Nevertheless, designing such types of algorithms for combinatorial optimization can be a daunting task that requires expertise on the problem. Because of this, the recent idea to infer heuristics without human intervention is an appealing objective. As demonstrated in  \cite{bello2016neural}, Reinforcement Learning (RL) can be used to that achieve that goal. In the Neural Combinatorial Optimization (NCO) framework, a heuristic is parameterized using a neural network to obtain solutions for many different combinatorial optimization problems without hand-engineering. The only requirement is that evaluating the objective function must not be time-consuming.
\\[3pt]
In this paper, we extend the work presented by Bello \cite{bello2016neural}, introducing constrained combinatorial problems into the NCO framework. So far, constrained problems have not been approached using this technique. Only combinatorial problems in which the neural network can be set up to ensure feasible solutions have been addressed. To that end, neural models have limited the action space the neural network produces to avoid dealing with unfeasible results. Usually, implementing a masking scheme over the output probability distribution. In this manuscript, we deal with constraints introducing them as penalty coefficients into the reward objective function. This way, it is possible to deal not only with maskable constraints but also with constraints that cannot be evaluated during the resolution process, broaden NCO to general constrained combinatorial problems.
\\[3pt]
To enhance this technology, we need better ways to infer a policy on the neural network. We also contribute to this sense. Unlike the original NCO proposal, where the solution is computed based on a single interaction with the environment, here, we consider the Constrained Markov Decision Process (CMDP) formulation for the problems. Therefore, the solution is generated as a sequence of decisions based on intermediate states obtained during the resolution process. This model uses the state representation to give the agent a better understanding of how the solution is evolving, thereby improving the quality of the results obtained.
\\[3pt]
To validate the proposed framework, we learn heuristics for two relevant and well-known operations research problems: Job Shop Scheduling (JSP)~\cite{garey1976complexity,chaudhry2016research} and Virtual Resource Allocation Problem (VRAP) \cite{beloglazov2012energy}. For both cases, constrained variants of the problems were considered. Conducted experiments point out that our model outperforms classical heuristic algorithms, metaheuristics, and the Constraint Programming (CP) solver CP-SAT from OR-Tools when real-time solutions need to be obtained. Moreover, the model shows a robust behavior, as the solutions' quality presents a low variance between different problem instances.

\section{Background}
\label{background}

The use of neural networks for solving combinatorial optimization problems dates back to \cite{hopfield1985neural}. The authors applied the Hopfield-network for solving instances of the Traveller Salesman Problem (TSP). Nevertheless, the application of neural networks on combinatorial problems was limited to small scale problem instances due to the available computational resources at that time. It has been in the last few years with the rise of deep learning that this topic has again attracted the attention of the artificial intelligence community.
\\[3pt]
Recently, \cite{vinyals2015pointer} trained a Deep Neural Network (DNN) to solve the Euclidean TSP using supervised learning. They proved that a neural network is able to parametrize a competitive policy also in domains with large action spaces as it is the case of most real-world combinatorial problems. To this end, they introduced the Pointer Network (PN), a neural architecture that enables permutations of the input sequence. Despite their positive results, using supervised learning to solve combinatorial problems is not trivial, as acquiring a training set implies the ability to solve a large number of instances optimally.
\\[3pt]
In \cite{bello2016neural}, the NCO framework was presented, and RL was implemented for the first time to solve combinatorial problems. The authors took the Pointer Network introduced by Vinyals and utilized it in an actor-critic architecture to solve the TSP problem. The work proved that it is possible to learn competitive heuristics without human intervention. Although, they applied heavy sampling and searching techniques at interference to improve the solution generated by the neural network itself.
In \cite{deudon2018learning}, the TSP problem was also optimized using the NCO approach. On this occasion, a Transformer Network \cite{vaswani2017attention} is used, a top performance architecture in Natural Language Processing.
In that work, the greedy output of the neural network is hybridized with a local search to infer better results. Independently, \cite{kool2018attention} also presented the same architecture, yet with improvements in the decoder as well as in the training mechanism. Benefits that allows them to be competitive without applying search strategies at inference.
\\[3pt]
All the works presented so far have major similarities: they are based on sequence-to-sequence models, architectures originally designed for supervised learning. In these models, the solution is decoded at once based only on a single interaction with the problem. Conversely, \cite{nazari2018reinforcement} approached the Vehicle Routing Problem (VRP) as an MDP. Specifically, they built the solution as a result of a sequence of decisions made interacting with the environment. Such an approach allows to focus on how the environment evolves to construct the solution. This strategy also enables to deal with stochastic versions of the problem.
\\[3pt]
So far, the literature has used NCO to solve combinatorial problems without dealing with constraints. E.g.,~\cite{mirhoseini2017device, nazari2018reinforcement, mao2016resource, mao2019learning} address scheduling problems relying on masking schemes to avoid unfeasible actions during the resolution process. Nevertheless, this cannot be done for many combinatorial problems. A different approach could be~\cite{chen2019learning}. That work deals with combinatorial problems performing an RL-assisted local search procedure. However, this method a is only viable in case a feasible solution is easy to find. In this paper, we extend NCO to deal with constraints in its formulation, allowing under this framework to compute rapid solutions even in highly constrained problems.

\section{Neural Constrained Combinatorial Optimization}
\label{neural_constrained_combinatorial_optimization}

In this section, we formally define NCO for solving constrained combinatorial problems. Firstly, we introduce the terminology to use in this manuscript. Let us represent each instance of the problem as a static feature vector $s \in \mathit{S}$, where $S$ stands for the whole distributions of instances of the problem the model needs to learn a heuristic. This vector $s$ defines the instance and does not change during the interaction with the environment. Let $d$ be the vector that represents the state of the environment. $d$ depicts the dynamic part of the input and evolves iteratively as partial decisions are made. The concatenation of those feature vectors at a time-step $t$ represents the input to our model $\{x_t \doteq (s,d_t), t=0,1,..,T\}$.
\\[5pt]\noindent
{\bf Limitations of Action-masked Networks.}
\label{limitations_of_action_masked_networks}
Current models used in NCO have limitations dealing with constraints. E.g., Pointer Networks (PNs)~\cite{vinyals2015pointer} can solve problems that require to compute permutations over the inputs (e.g., the TSP and the Knapsack problem). However, they are not directly applicable to other combinatorial problems. Other approaches incorporate a problem-specific masking scheme to avoid the infeasibilities the problem produces. It is the case  of~\cite{nazari2018reinforcement} for solving the VRP, the cities previously visited are masked to avoid selecting them later in the decision process.
\\[3pt]
Using masking schemes forces the neural model to produce feasible solutions and, therefore, ensures that the environment can evaluate the solution (and provide a reward signal). Nevertheless, masking schemes cannot be applied to all constraint problems.
{\it It can be used only in problems in which the construction produces a valid solution.} However, for general combinatorial problems with constraint equations, which feasibility can be verified only at the end of the episode (when the solution is obtained) is a hard task.
Therefore, in this work, we address these constrained problems defining them as Constrained Markov Decision Processes (CMDP)~\cite{altman1999constrained}. In this framework, the environment provides a reward signal and penalty signals generated from constraint dissatisfaction. This way, constraints can be incorporated as penalty terms into the objective function~\cite{borkar2005actor} and guide the agent to achieve feasible solutions.
\\[3pt]
\textbf{Remark 1:} Dealing with constraints as penalties is a key point in highly constrained environments. Providing bad rewards to unfeasible solutions can flatten the objective function, which leads to a lack of information to infer a competitive policy. Without this relaxation technique, it would be near impossible for the agent to achieve a feasible region, as it would not experience enough positive rewards. The problem of sparse reward is well known in RL~\cite{jaderberg2016reinforcement}.
\\[5pt]\noindent
{\bf Reward constrained policy optimization.}
\label{reward_constrained_policy_optimization}
Reward constrained policy optimization method \cite{tessler2018reward, paternain2019constrained} requires a parametrization of the policy, as it is over the objective expected reward function where the penalty is added. In particular, we resort to Policy Gradients to learn the parameters of the stochastic policy $\pi_\theta(y|x)$ that, given as input the tuple composed by the instance of the problem and the state of the environment $x_t=\{s,d_t\}$, assigns high probabilities to actions $y$ that produce solutions with high reward, and low probabilities to those that do not. In Policy Gradients, the objective function $J_R^\pi(\theta)$ is defined as the expected reward for the policy $\pi$
\begin{equation}
\label{eq:1}
  J_R^\pi(\theta) =   \textstyle{\mathbb{E}}_{\tau \sim \pi_{\theta}}[R(\tau)].
\end{equation}
Non-maskable constraints are incorporated into (\ref{eq:1}) using the Lagrange relaxation technique. This allows us to shape the objective function, proportionally penalizing those policies that lead to infeasibilities. But first, for each constraint signal $C_i$, we define its expectation of dissatisfaction associated to the policy $\pi$ as
\begin{equation}
  J_{C_i}^\pi(\theta) =   \textstyle{\mathbb{E}}_{\tau \sim \pi_{\theta}}[C_i(\tau)].
\end{equation}
The primal problem becomes then to find the policy $\pi$ that maximizes the expected reward subject to the satisfactions of the constraints
\begin{equation}
\label{eq:9}
   \textstyle{\max}_{\pi \sim \Pi} J_R^\pi(\theta) \quad  \textrm{ s.t. } \quad J_{C_i}^\pi \leq 0 \quad \forall i.
\end{equation}
Using the Lagrange relaxation technique \cite{bertsekas1997nonlinear}, the problem statement in (\ref{eq:9}) is reformulated as an unconstrained problem where the unfeasible solutions are penalized. The objective function is therefore defined as
\begin{equation}
\label{eq:11}
g(\lambda)=  \textstyle{\max}_{\theta} J_L^\pi(\lambda, \theta)  =   \textstyle{\max}_{\theta} [J_R^\pi(\theta) - \textstyle{\sum}_{i} \lambda_i \cdot J_{C_i}^\pi(\theta)]  = \max_{\theta} [J_R^\pi(\theta) - J_{\xi}^\pi(\theta)],
\end{equation}
where $J_L^\pi(\lambda, \theta)$ denotes the Lagrangian objective function, $g(\lambda)$ stands for the Lagrange dual function, and $\lambda_i$ are the Lagrange multipliers, i.e., penalty coefficients. In this equation, we introduce the term $J_{\xi}^\pi(\theta)$ that defines the expected penalization, computed as the weighted sum of all expectation of constraint dissatisfaction signals.

It is noteworthy that setting the Lagrange coefficients $\lambda_i$ is a multi-objective problem where there exists a different optimum solution for each configuration. In this sense, we perform a manual selection of the penalty coefficients, although the optimal value can also be obtained using other alternatives, e.g., a multi-time scale learning \cite{tessler2018reward}.

The gradient of the Lagrangian objective function $J_L^\pi(\theta)$ is derived using the log-likelihood method. This derivation process is similar as deriving the expected reward, method introduced in \cite{williams1992simple}. The resulting gradient equation is
\begin{equation}
    \nabla_{\theta}J_L^\pi(\theta) =   \textstyle{\mathbb{E}}_{\tau \sim \pi_{\theta}(\cdot|s)}[(L(y|x) \cdot \nabla_{\theta} \log\pi_{\theta}(y|x)],
\end{equation}
where
$  L(y|x)  = R(y|x) - \xi(y|x)
             = R(y|x) - \textstyle{\sum}_{i} \lambda_i \cdot C_i(y|x)
$
denotes the penalized reward obtained in each iteration,
calculated subtracting from the reward signal $R(y|x)$ the weighed sum of all the constrained dissatisfaction signals $C(y|x)$. Lastly, the gradient is approximated via Monte-Carlo sampling, where $B$ problem instances are drawn from the problem distribution $s_{1},s_{2},\ldots,s_{B} \sim \mathcal{S}$. To reduce the variance of the gradients, and therefore, to speed up the convergence, we include a baseline estimator $b(x)$. Finally, the gradient of the Lagrangian function is defined as
\begin{equation}
    \nabla_{\theta}J_L^\pi(\theta) \approx \tfrac{1}{B} \textstyle{\sum}_{j=1}^{B} (L(y_{j}|x_{j}) -b(x_{j})) \cdot\nabla_{\theta} \log\pi_{\theta}(y_j|x_{j}).
\end{equation}
\noindent
{\bf Self-competing baseline estimator.}
\label{baseline_estimator}
The baseline function $b(x)$ estimates the reward the model achieves for a problem input $x$, such that the current result obtained for the instance $L^\pi(y|x)$ can be compared to the performance of
$\pi$. The baseline estimator can be as simple as a moving average $b(x) = M$ with decay $\beta$, where $M$ equals $L^\pi$ in the first iteration, and updates as $M \leftarrow \beta M + (1-\beta) L^\pi$ in the following ones. A popular alternative is the use of a learned value function or critic $\hat{v}(x, \theta_{\nu})$, where the parameters $\theta_{\nu}$ are learnt from observations \cite{grondman2012survey}. A baseline estimator performs in the following way, the advantage function $L^\pi(y|x) - b(x)$ is positive if the sampled solution is better that the baseline, causing these actions to be reinforced, and vice-versa.
\\[3pt]
Here, we propose a new baseline based on estimations over the current stochastic policy. This method allows us to not rely on an additional estimator for computing the baseline. In this approach, we increase the learning batch $B$ introducing $N$ times every instance of the problem. Since during the learning process the policy is stochastic, for every instance we obtain $N$ different solutions. This creates the reward distribution $Q_j^N$ we use the to estimate the current performance of the model on the instance $x_j$. In particular, we select the baseline estimator $b(x)$ as the quantile (e.g.: $\alpha=0.1$) of the obtained distribution. The baseline is therefore calculated as
\begin{equation}
    b(x_j)=\{q_j: Pr(Q_j^N \leq q_j) = \alpha\}.
\end{equation}

\section{Neural Network Model}
\label{neural_network_model}

In this paper, we argue that sequence-to-sequence models~\cite{sutskever2014sequence} compute the solution for combinatorial problems without interacting with the environment. Those models receive an instance of the problem and build a solution based only on the hidden state the decoder stores. By contrast, the proposed neural network presents similarities with traditional RL models used for solving fully observable MDPs. In those cases, computing the action distribution is not required to store any previous information, and therefore, memory-less architectures can be used for this purpose.
\\[3pt]
\textbf{Remark 2:} We argue that combinatorial problems can be defined as a fully observable CMDP. Since the solution is iteratively built interacting with the problem, partial solutions can be evaluated to give the agent a reference on how the solution evolves on the problem. In that perspective, the Recurrent Neural Network (RNN) used in the decoder on sequence-to-sequence models can be substituted by a memory-less DNN. This benefits the results as accessing the fully observable state of the problem is more reliable than doing on memories.
\\[5pt]\noindent
{\bf The proposed model.}
\label{proposed_model}
The model consists of two main components: an embedding and encoding part and a DNN in charge of computing the output distribution. The encoder codifies the instance of the problem $s$, which may be formed by sequences of different lengths. This corresponds to the static part of the input to our model and does not change during its resolution of the problem. The vector $s$ is combined with the state of the environment $d_t$ to create the input $x_t=\{s,d_t\}$ from which a DNN computes the action distribution. This part of the model is required to be computed in every interaction. It constitutes, therefore, the dynamic part of the model, and it is evaluated until the whole solution is completed.
\\[5pt]\noindent
{\bf State-based attention mechanism.}
\label{attention_mechanism}
Having access to state representation, allows to build a \textit{glimpse} mechanism over the state representation $d_t$. In this sense, this state-based attention mechanism extracts key features from the state representation $\hat{d}_t$ and uses this information to create a context vector $c_t$~\cite{bahdanau2014neural, luong2015effective} that introduces key information deeper into the model.

\section{Experimentation}
\label{experimentation}

To validate the proposed framework, we optimize two relevant and well-known constrained combinatorial problems: a Job Shop Problem~\cite{garey1976complexity,chaudhry2016research} and a Resource Allocation Problem~\cite{beloglazov2012energy}. In both cases, there exist a huge number of variants in the literature. For the sake of evaluating the potential of the proposed model, we select variants of the problems above that present both types of constraints we argue in this paper: constraints that can be embedded into the model and constraints that need to be relaxed and incorporated into the objective function.

\subsection{Job Shop Problem with limited idle time}

In the Job Shop Problem (JSP) there exist a number of $n$ jobs  $J=\{J_0,J_1...J_{n-1}\}$ and a set $m$ machines $M = \{M_0,M_1...M_{m-1}\}$. Within every job $J_i$ there is a number of operations $O_i$ that need to be processed in a specific order $O_i = \{O_{i,0},O_{i,1}...O_{i,m-1}\}$. For each operation $O_{i,j}$, the machine $M_{i,j}$ and the duration time $D_{i,j}$ associated are defined. The aim of this problem consists of assigning the jobs to the machines such that the operation period is minimized, also known as the \textit{makespan}. The classical JSP presents two types of constraints:
\begin{itemize}[noitemsep, topsep=0pt, label={-}]
  \item Precedence constraints: specify that for every two consecutive operations in a job, the first one must be completed before the second one can be scheduled.
  \item No overlap constraints: these constraints arise from the fact that a machine can only work in one operation at a time.
\end{itemize}
These constraints can be managed via a masking scheme, so we implement them as hard-constraints in our model. To include non-maskable restrictions, the JSP variant with {\it limited idle time} was considered. Under that constraint, for any machine, the period between finishing operation and starting the next operation (idle time) cannot exceed a certain threshold $T_{th}$. This constraint arises naturally in a real context, as the aim is usually to maximize the productivity of the machinery.
\\[3pt]
In the JSP with limited idle time, the objective function can be penalized by the sum of all intervals in which the idle time between operations exceeds the threshold $T_{th}$. Hence, the objective function to minimize is defined as
\begin{equation}
  L = \max {\mathcal{M}_i} + \lambda \textstyle{\sum}_{i,j}((t^{start}_{O_{i,j}} - t^{end}_{O_{i,j-1}})-T_{th})^+ ,
\end{equation}
where $\mathcal{M}_i$ denotes the time until the job $J_i$ is finished, and $t^{start}_{O_{i,j}}$ and $t^{end}_{O_{i,j}}$ the start and ending time scheduled for the operations.

\paragraph{Particularized model.}
\label{particularized_model}

The particularized neural model for the JSP problem is depicted in Fig.~\ref{fig:2}. The model at each time-step $t$, computes a binary action deciding whether the next operation for each job is scheduled. To this end, the model stores an index vector $i_t$ pointing at the operations that are required to be scheduled next. Remember that the operations for a job must be assigned in a specific order; that is operation cannot be scheduled until the previous one has finished. This procedure is repeated until all operations are assigned.

\begin{wrapfigure}{r}{0.5\textwidth}
\vskip -25pt
$\ $
\begin{center}
\centerline{\includegraphics[width=0.5\columnwidth]{2.pdf}}
\caption{Neural model particularized for the JSP problem. The model is formed by a single LSTM encoder that operates over the sequence of operations for each job. The resulting vector that describes the combinatorial problem is combined with the state of the environment to decide the operations to be scheduled at each time-step $t$.}
\label{fig:2}
\end{center}
\vskip -0.38in
\end{wrapfigure}

As introduced, an instance of the JSP problem is defined
by the machine assignation $M_{ij}$ and the time duration $D_{ij}$ matrices. For each operation $O_{ij}$, these values are concatenated to create the static input, denoted as $s_{ij}$ in the paper. This vector is embedded and sequentially encoded. In this case, the encoding process is configured backward for this problem. Producing, therefore, for each operation, a representation of the remaining operations until the job is completed. We refer to this vector as $e_{ij} = \mathit{enc}(s_{ij},..,s_{im}) \: \forall i$.
\\[3pt]
The state of the problem is defined by the state of the machines and the operations currently being process at the decision time. We represent the state using two vectors: the first one indicates the number of time units until the machines are released, and the second one, the time left for the previous operation to finish. Those vectors constitute the dynamic part of the input $d_t$, and are recomputed at each time-step $t$.

\begin{wrapfigure}{r}{0.4\textwidth}
\vskip -20pt
\centering
\footnotesize
\tiny

JSP 10x10

     {\includegraphics[width=.4\textwidth,
    trim={0 10pt 35pt 20pt},clip]{3_a.pdf}}

\vskip 3pt
JSP 15x15

 {    \includegraphics[width=.4\textwidth,
    trim={0 10pt 35pt 20pt},clip]{3_b.pdf}
    }

\vskip-10pt
  \caption{Comparison of the distance to the optimal solution in the \textit{classic} JSP between: different heuristics, a metaheuristic GA and our RL approach with sampling applied.}\label{fig:3}
  \vskip-40pt
\end{wrapfigure}

Both parts, the static and the dynamic state, are concatenated to create the input $x_t = (s,d_t)$ from where the DNN computes the output probability distribution (depicted in \textit{red} in Fig.~\ref{fig:2}). In this example, the output corresponds to a Bernoulli distribution, which indicates for each job whether the current operation (pointed by $i_t$) should be scheduled. Nevertheless, not every action can be selected at any time. Actions that lead to an infeasibility in the \textit{precedence} and \textit{no overlap constraint} are masked. This is achieved by forcing to zero the probability of scheduling the operation. In order to build the mask, the required information indicating whether the previous operation has finished or a machine is free to use can easily be gathered from the state vector $d_t$.
\\[3pt]
Finally, the model presents a double glimpse mechanism: one on operations to be scheduled, and another over the state representation. The first one corresponds to the context vector $c_t$, generated gathering from $e_{ij}$ the indices pointed by the vector $i_t$. It acts as a glimpse on the operations yet to be scheduled in each job. The second one corresponds to the time for the previous operation to finish $\hat{d}_t$. Those vectors are introduced into the model, enhancing the features from which the solution is computed.

\subsection{Resource Allocation Problem}
\label{resource_allocation_problem}

In addition to the JSP, to prove the validity of the proposed framework, we evaluate its performance on the Virtual Resource Allocation Problem (VRAP)~\cite{beloglazov2012energy}. In this problem, a set of services is required to be allocated in a pool of server nodes. A service is composed of a chain of Virtual Machines (VM) that need to process a flow of information to fulfill a task. Here, the objective function to minimize is the energy cost of the entire set-up. This is calculated as the sum of the energy required to power up the servers plus the energy consumption of the virtual machines. Therefore, the problem is to arrange the services in the smallest number of nodes yet meeting the constraints associated with the infrastructure capacity and the service itself (e.g., maximum service latency). Further details on the problem can be found in Appendix B.

\begin{wrapfigure}{r}{0.4\textwidth}
\vskip -10pt
\centering

  \includegraphics[width=0.4\textwidth]{4.pdf}

  \caption{Mean objective value in function of the time obtained by OR-Tools for the JSP \textit{with limited idle time} ($\lambda=1$). The performance of the RL model is depicted in dashed line. The intersections between both representations are highlighted to indicate the time required for the solver to match the RL model.}
  \label{fig:4}

  \includegraphics[width=0.4\textwidth]{5.pdf}

  \caption{Comparison of the distance to the optimal solution in the Resource Allocation Problem between a Genetic Algorithm and our RL model.}
  \label{fig:5}
\vskip-30pt
\end{wrapfigure}

The RL model we propose to address this problem is similar to the previous one. The service composed by a sequence of VMs, each one represented by its specific features, is encoded using an RNN. The resulting vector represents the static part of our input. It is combined with the state of the environment to feed the neural network that iteratively decides the server in which each VM in the chain is going to be located. Here, the physical resource constraints can be guaranteed by the model. Nevertheless, the restrictions associated with the whole service (e.g., end-to-end latency) cannot be checked until the solution is commuted. Therefore, and as shown in the previous example, these constraints are relaxed and introduced as penalty terms into the objective function.

\begin{table*}[t]
\caption{Average objective, standard deviation and mean computing time for instances of the \textit{classic} JSP ($\lambda=0$) and JSP  \textit{with limited idle time} ($\lambda=1$). The size of the instance is denoted by the number of jobs $n$ and the number of machines $m$: JSP $n$x$m$.}
\label{tab:1}
\vskip -2pt
\centering
\scriptsize
\begin{sc}
\begin{tabular}{lcccccccccccc}
\toprule

Method      & \multicolumn{3}{c}{JSP10x10}                                      & \multicolumn{3}{c}{JSP15x15}                                       & \multicolumn{3}{c}{JSP20x20}                                       & \multicolumn{3}{c}{JSP25x25}                                       \\
\midrule
  ($\lambda = 0$)    & mean                 & std                  & time                 & mean                 & std                  & time                 & mean                 & std                  & time                 & mean                 & std                  & time                 \\
\midrule
SPT & \multicolumn{1}{r}{99.9} & \multicolumn{1}{r}{9.1} & \multicolumn{1}{r}{0.005s} & \multicolumn{1}{r}{153.2} & \multicolumn{1}{r}{10.5} & \multicolumn{1}{r}{0.012s} & \multicolumn{1}{r}{198.3} & \multicolumn{1}{r}{9.3} & \multicolumn{1}{r}{0.022s} & \multicolumn{1}{r}{252.9} & \multicolumn{1}{r}{13.2} & \multicolumn{1}{r}{0.045s}\\

LPT & \multicolumn{1}{r}{107.8} & \multicolumn{1}{r}{9.7} & \multicolumn{1}{r}{0.005s} & \multicolumn{1}{r}{163.9} & \multicolumn{1}{r}{10.9} & \multicolumn{1}{r}{0.012s} & \multicolumn{1}{r}{218.8} & \multicolumn{1}{r}{13.7} & \multicolumn{1}{r}{0.023s} & \multicolumn{1}{r}{278.2} & \multicolumn{1}{r}{17.2} & \multicolumn{1}{r}{0.050s}\\

FCFS & \multicolumn{1}{r}{107.1} & \multicolumn{1}{r}{10.0} & \multicolumn{1}{r}{0.005s} & \multicolumn{1}{r}{163.2} & \multicolumn{1}{r}{13.7} & \multicolumn{1}{r}{0.012s} & \multicolumn{1}{r}{219.3} & \multicolumn{1}{r}{13.1} & \multicolumn{1}{r}{0.023s} & \multicolumn{1}{r}{276.9} & \multicolumn{1}{r}{14.7} & \multicolumn{1}{r}{0.051s}\\

LWR & \multicolumn{1}{r}{113.9} & \multicolumn{1}{r}{14.1} & \multicolumn{1}{r}{0.037s} & \multicolumn{1}{r}{174.8} & \multicolumn{1}{r}{12.6} & \multicolumn{1}{r}{0.123s} & \multicolumn{1}{r}{227.3} & \multicolumn{1}{r}{12.7} & \multicolumn{1}{r}{0.279s} & \multicolumn{1}{r}{287.2} & \multicolumn{1}{r}{18.0} & \multicolumn{1}{r}{0.598s}\\

GA\_P(300)  & \multicolumn{1}{r}{96.4} & \multicolumn{1}{r}{5.2} & \multicolumn{1}{r}{55.80s} & \multicolumn{1}{r}{169.4} & \multicolumn{1}{r}{6.7} & \multicolumn{1}{r}{165.8s} & \multicolumn{1}{r}{254.3} & \multicolumn{1}{r}{7.2} & \multicolumn{1}{r}{303.9s} & \multicolumn{1}{r}{338.2} & \multicolumn{1}{r}{7.6} & \multicolumn{1}{r}{586.0s}\\

RL\_S(1) & \multicolumn{1}{r}{101.3} & \multicolumn{1}{r}{8.5} & \multicolumn{1}{r}{0.83s} & \multicolumn{1}{r}{161.9} & \multicolumn{1}{r}{11.9} & \multicolumn{1}{r}{2.15s} & \multicolumn{1}{r}{216.2} & \multicolumn{1}{r}{14.4} & \multicolumn{1}{r}{3.56s} & \multicolumn{1}{r}{277.2} & \multicolumn{1}{r}{17.3} & \multicolumn{1}{r}{5.16s}\\

RL\_S(40) & \multicolumn{1}{r}{91.9} & \multicolumn{1}{r}{5.8} & \multicolumn{1}{r}{1.04s} & \multicolumn{1}{r}{143.6} & \multicolumn{1}{r}{6.4} & \multicolumn{1}{r}{2.31s} & \multicolumn{1}{r}{196.9} & \multicolumn{1}{r}{7.7} & \multicolumn{1}{r}{4.38s} & \multicolumn{1}{r}{249.3} & \multicolumn{1}{r}{7.7} & \multicolumn{1}{r}{6.38s}\\

RL\_S(100) & \multicolumn{1}{r}{90.7} & \multicolumn{1}{r}{5.4} & \multicolumn{1}{r}{1.17s} & \multicolumn{1}{r}{142.1} & \multicolumn{1}{r}{6.6} & \multicolumn{1}{r}{2.65s} & \multicolumn{1}{r}{193.6} & \multicolumn{1}{r}{8.0} & \multicolumn{1}{r}{4.52s} & \multicolumn{1}{r}{244.5} & \multicolumn{1}{r}{8.1} & \multicolumn{1}{r}{7.04s}\\

OR-Tools & \multicolumn{1}{r}{81.5} & \multicolumn{1}{r}{4.6} & \multicolumn{1}{r}{0.082s} & \multicolumn{1}{r}{118.8} & \multicolumn{1}{r}{4.4} & \multicolumn{1}{r}{61.22s} & \multicolumn{1}{r}{156.2} & \multicolumn{1}{r}{4.5} & \multicolumn{1}{r}{1h(*)} & \multicolumn{1}{r}{195.4} & \multicolumn{1}{r}{4.9} & \multicolumn{1}{r}{1h(*)}\\

\midrule
 ($\lambda = 1$)     & mean                 & std                  & time                 & mean                 & std                  & time                 & mean                 & std                  & time                 & mean                 & std                  & time                 \\
\midrule

GA\_P(300)  & \multicolumn{1}{r}{343.7} & \multicolumn{1}{r}{45.5} & \multicolumn{1}{r}{91.6s} & \multicolumn{1}{r}{1117.0} & \multicolumn{1}{r}{39.0} & \multicolumn{1}{r}{257.3s} & \multicolumn{1}{r}{2476.0} & \multicolumn{1}{r}{62.6} & \multicolumn{1}{r}{578.4s} & \multicolumn{1}{r}{4453.2} & \multicolumn{1}{r}{111.7} & \multicolumn{1}{r}{1079s}\\

RL\_S(40) & \multicolumn{1}{r}{360.4} & \multicolumn{1}{r}{38.7} & \multicolumn{1}{r}{1.36s} & \multicolumn{1}{r}{860.2} & \multicolumn{1}{r}{65.4} & \multicolumn{1}{r}{3.12s} & \multicolumn{1}{r}{1573.0} & \multicolumn{1}{r}{112.7} & \multicolumn{1}{r}{5.18s} & \multicolumn{1}{r}{2745.0} & \multicolumn{1}{r}{173.1} & \multicolumn{1}{r}{7.75s}\\

OR-Tools & \multicolumn{1}{r}{221.4} & \multicolumn{1}{r}{18.4} & \multicolumn{1}{r}{1h(*)} & \multicolumn{1}{r}{593.5} & \multicolumn{1}{r}{19.9} & \multicolumn{1}{r}{1h(*)} & \multicolumn{1}{r}{1221.0} & \multicolumn{1}{r}{50.9} & \multicolumn{1}{r}{1h(*)} & \multicolumn{1}{r}{2075.0} & \multicolumn{1}{r}{101.2} & \multicolumn{1}{r}{1h(*)}\\

\bottomrule
\end{tabular}
\end{sc}
\begin{small}
\quad (*) The result is not optimal, the execution has been forced to end after the indicated time.
\end{small}
\vskip -0.1in
\end{table*}

\subsection{Results and analysis}
\label{results}

\paragraph{Job Shop Problem.}
\label{experimentation_job_shop}
We present the experimental study on the classical Job Shop Problem ($\lambda=0$) and also on the \textit{limited idle time} variant ($\lambda > 0$). The results obtained by our framework are compared with a Genetic Algorithm (GA)~\cite{cheng1996tutorial} and the solver CP-SAT from OR-Tools~\cite{CP}. In addition, in the case of the classic JSP results are also compared with some well-know heuristics: the \textit{Shortest Processing Time} (SPT),  \textit{Longest Processing Time} (LPT), \textit{First-come-first-served} (FCFS) and \textit{Least Work Remaining} (LWR)~\cite{mahadevan2010operations}.
\\[3pt]
In the experimentation, two different decoding mechanisms are used in the RL model: a greedy and a sampling technique. In the greedy approach, the solution is directly obtained from the model, whereas in the sampling method, multiple solutions are computed from the stochastic policy, and the best one is selected. This comes naturally in this proposal with the self-competing strategy, therefore it does not add overhead to the model. These instances are referenced as RL\_S followed by the number of solutions taken in the experiment.
\\[3pt]
As noted, the results of the model are also compared with those obtained by OR-Tools. For small size instances, the solver is able to compute the optimal solution. However, for larger instances or when the number of restrictions is higher, as it is the case of the \textit{limited idle time} variant, computing the optimal solution becomes intractable. In those cases, we limited the execution time up to one hour, and the solutions obtained are only considered as near-optimal approximations.
\\[3pt]
To implement the model, we use LSTM~\cite{gers1999learning} neural networks in the recurrent encoder and the objective function is optimized using Adam \cite{kingma2014adam}. The implementation details can be found in Appendix~A.
\\[3pt]
The results are summarized in \tablename~\ref{tab:1}. It introduces the average objective, the standard deviation and the mean computation time obtained by the different methods for the classic JSP $(\lambda=0)$ and the JSP \textit{with limited idle time} $(\lambda=1)$.  Performance measures were averaged on a set of 50 instances for each problem size. We observe that in the classic JSP, our approach is competitive in terms of the quality of the solution against the compared heuristics and the GA, especially for small and medium problems instances JSP10x10 and JSP15x15. Moreover, the variance obtained by the RL model is considerably low during the tests. We conclude, therefore, that the model is robust in the sense that the results are consistent in performance. It can also be observed that the sampling technique RL\_S(40) provides a reasonable tradeoff between the computational cost and the improvement in the results. It is, therefore, the number of samples we used hereof.
\\[3pt]
Fig.~\ref{fig:3} shows the optimality gap for the instances in which the optimal solution can be obtained in a reasonable time: JSP10x10 and JSP15x15. The optimality gap is defined as the difference (in percent) between the solution obtained and the optimum. According to the results, the RL\_S(40) outperforms the rest of the heuristics and metaheuristics. However, it presents a difference in performance when compared to the solver that goes from 11.2\% in the JSP10x10 up to 27.5\% in the JSP25x25 (see \tablename~\ref{tab:1}).
\\[3pt]
Although the solver performed better than the RL model, the time required in each case is totally different, so carrying out a fair comparison is tricky. For this reason, we compute the time required by the solver to obtain a solution comparable with that of the RL model. We observe that in the case of the classical JSP ($\lambda=0$), the solver outperforms the other alternatives, and it does in a competitive computational time. Nevertheless, things turn around when the \textit{limited idle time} variant is considered. Although this problem adds no much complexity, the computation time required by OR-Tools increases significantly, in this case, a slight increase in the number of constraints in the problem is enough to prevent the solver from getting good approximations in the short time.
To illustrate that, we depict in Fig.~\ref{fig:4} the performance of the solver in function of the time elapsed. For the problem JSP15x15 and larger,
the time required by the solver to match the performance of the RL model is orders of magnitude higher than the inference time. This becomes the RL model competitive for achieving rapid solutions.
\\[3pt]
{\bf Resource Allocation Problem.}
\label{resource_allocation_problem_experimentation}
The VRAP presents some differences when compared to the JSP. In this problem, services are located one at a time, and they are formed by sequences of no more than a few virtual machines. Also, the output is a categorical distribution over the servers in the infrastructure instead of binary decisions. These factors make it easier for the model to extract features from the problem definition. This point is reflected in a lower number of parameters of the neural network and faster training times. Further details on the implementation of the model can be seen in Appendix B.
\\[3pt]
In this problem, we compare the performance of the RL model with a GA and the OR-Tools CP solver. We depict the results in Fig.~\ref{fig:5}. In this case, we are able to compute the optimum with the solver, so the results are given relative to it. As shown in the picture, the RL model consistently predicts close to the optimal allocation sequences, outperforming the GA. In this case, the model is able to extract the features of the infrastructure and the services in order to infer a policy that almost suits perfectly on the problem instances.

\section{Discussion and Conclusion}
\label{discussion_and_conclusion}

In this work, we extend Neural Combinatorial Optimization (NCO) to include constrained combinatorial problems. We do it defining them as fully observable Constrained Markov Decision Processes (CMDP), where the proposed model iteratively constructs a solution based on the intermediate states obtained during the resolution process. To that end, in addition to the reward signal, the model relies on penalty signals generated from constraint dissatisfaction to guide the agent to achieve feasible solutions. This approach benefits from not requiring memory-based architectures to compute the solutions, which improves the quality of the results obtained.
\\[3pt]
The conducted experimental study points out that the proposed architecture presents the required versatility for being applied to real-world constraint combinatorial problems. Moreover, obtained results indicate that the RL model outperforms classical heuristics, metaheuristic, and CP solvers when real-time solutions need to be obtained.
\\[3pt]
Scaling the model for larger instances is an important direction for future research. We observe that the larger the combinatorial problem is, the harder it is for the model to infer good policies for the instances. The RL model tends to generalize well, but the performance gap becomes larger as the size of the problem increases.

\iffalse
\newpage
\section*{Broader Impacts}

Combinatorial Optimization is being solved by thousands of companies around the world every single day. In some instances, they can wait for hours to obtain sufficiently good solutions, but more and more industrial players rely on the ability to get fast (sometimes in real-time) high accurate solutions.

This paper addresses the need to obtain solutions for some combinatorial optimization, where a simple masking strategy cannot be used to guarantee a solution's feasibility. We want to stress that this is the first step towards much more complicated settings (i.e., stochastic settings or partially observable settings, to name a few) that both academicians and, more importantly, industrial partners would seek to be able to solve.

Obtaining better solutions quicker can significantly reduce costs and make them more competitive and, in some cases, more environmentally friendly.

\fi

\begin{ack}
This work was partially supported by the U.S. National Science Foundation, under award numbers
NSF:CCF:1618717, NSF:CMMI:1663256 and NSF:CCF:1740796, by the Basque Government Research Group IT1244-19 and by the Spanish Ministry of Science under the project TIN2016-78365-R.
\end{ack}

\bibliographystyle{abbrvnat}

redpajama_set_name: RedPajamaArXiv


text:
Protestant Infirmary in 1888 with Horse-Drawn Ambulance in Front. (From the Lexington Herald, Lexington, Kentucky, 5 June 1938)
Dr. J. M. Skillman, Dr. David Barrow, Dr. F. H. Clark, Dr. B. L. Coleman, Dr. J. C. Carrick, Dr. John W. Scott, Dr. G. D. Kelly, and Dr. W. O. Bullock. (From the Lexington Herald, Lexington, Kentucky, 5 June 1938)
(From the Postcard Collection, Special Collections, University of Kentucky, Lexington, Kentucky)
In 1888, a group of women from the Guild of Christ Church, Episcopal, established the Protestant Infirmary in Lexington, Kentucky. Shortly thereafter, the Protestant Infirmary became the Good Samaritan Hospital. The original plant of the Infirmary came at a cost of nearly $40,000. Some fifty years later, that value had increased to more than one million dollars. The first published annual report in 1889 revealed that 659 patients had been cared for with a budget of $11,000. The original trustees of the institution included Mark Collis, Mary Goodloe, T. T. Forman, David C. Frost, Ida Withers Harrison, J. R. Howard, John Lewis, W. L. Threlkeld, J. L. Watkins, and Milford White. The medical staff included consulting physician Dr. H. M. Skillman, consulting surgeon Dr. David Barrow, and doctors F. H. Clark, B. L. Coleman, and J. C. Carrick; surgeons included doctors John W. Scott, G. D. Kelly, and W. O. Bullock.
Private Room in the Good Samaritan Hospital in 1899. (From the Lexington Herald, Lexington, Kentucky, 20 June 1954)
Over time, it was realized that there was a need for a nurses' training school at the hospital. In 1893 the first class of graduate nurses included four women; Miss Anna Dorcas, who went on to serve as superintendent of the hospital and Mrs. Sarah Mock. The school produced six graduate nurses in 1895, two in 1897, and ten in 1899. In the winter of 1895, the infirmary was transferred by the women of the Episcopal Church to all of the protestant churches of the city of Lexington and was renamed Good Samaritan Hospital. Control of the hospital was held by a board of trustees that included two representatives from each of the five protestant denominations (Baptist, Christian, Episcopal, Methodist, and Presbyterian). The newly renamed hospital stayed at its original location until 1907 when it moved into a brand new building on South Limestone in Lexington. The new hospital contained 130 patient beds. Superintendents at the 'old' institutions were Miss Janet Bahring, Mrs. May McIntyre, and Miss Anna Dorcas. Miss Mary Shavers served as the first superintendent of the new hospital's location until 1912. She was succeeded by Miss Josephine F. Royden. Miss Alberta Dozier was a temporary superintendent in 1925 until she was succeeded by Miss Lake Johnson. It was under Johnson's management in which significant growth of the hospital was made.
By the summer of 1914, the nurses' home had been completed and nurses moved into the new building. In the winter of 1925, the hospital was taken over by the General Hospital Board of the Southern Methodist Church. The next year saw a new heating plant followed by a five-story, fireproof annex that had 36 private beds, an obstetrical department, an operating pavilion that had six operating rooms, and an x-ray and radiotherapy departments.
In 1926, the Crippled Children's Hospital, managed by the Shriner's in conjunction with Good Samaritan Hospital, was built. Nearing the end of the 1920s, an addition was built to the nurses' home with 32 additional beds for nurses. In 1930, the Mary A. Ott building was completed and contained 40 rooms. By 1954 a new "south" wing had been erected and dedicated at the hospital at a cost of about one million dollars. The new addition included advanced medical technology and specialized operating theaters, including one for orthopedic operations. The number of operating rooms in the hospital jumped to seventeen. The total number of patient beds ballooned to 385, making it the second-largest general hospital in the state of Kentucky. The new "south" wing housed patients on the first and second floors, the third flood contained labor and delivery rooms for maternity patients, the fourth floor was devoted to a surgical suite, and the fifth floor held rooms for orthopedic patients. Oxygen was piped directly into patient rooms and an "Executone" call system allowed nurses to talk with patients remotely in their rooms.
(From the James Edwin Weddle Photographic Collection, University of Kentucky, Lexington, Kentucky)
The hospital continued to see steady growth throughout the remainder of the 20th century. In 2007, the renamed "Samaritan Hospital" filed for bankruptcy protection. However, the University of Kentucky began footing the bill for the hospital's daily expenses and eventually took over its management of daily operations. As of 2019, Good Samaritan Hospital still operates under the auspices of the University of Kentucky Healthcare.
Good Samaritan Image Gallery
(From the Lafayette Studios Photographs: 1930s-1940s Decades, University of Kentucky, Lexington, Kentucky)
(From the Louis Edward Nollau Nitrate Photographic Print Collection, University of Kentucky, Lexington, Kentucky)
The Lexington Herald (Lexington, Kentucky), 5 June 1938, p. 32.
Lexington Herald-Leader (Lexington, Kentucky), 7 June 2007.
Weddle, James Edwin. The James Edwin Weddle Photographic Collection, University of Kentucky, Lexington, Kentucky
Nollau, Louis Edward. The Louis Edward Nollau Nitrate Photographic Print Collection, University of Kentucky, Lexington, Kentucky
Lafayette Studios. The Lafayette Studios Photographs: 1930s-1940s Decades, University of Kentucky, Lexington, Kentucky

redpajama_set_name: RedPajamaCommonCrawl


Crown Fountain is an interactive work of public art and video sculpture featured in Chicago's Millennium Park, which is located in the Loop community area. Designed by Catalan artist Jaume Plensa, it opened in July 2004. The fountain is composed of a black granite reflecting pool placed between a pair of glass brick towers. The towers are 50 feet (15.2m) tall and use light-emitting diodes (LEDs) to display digital videos on their inward faces. Weather permitting, the water operates from May to October, intermittently cascading down the two towers and spouting through a nozzle on each tower's front face. Residents and critics have praised the fountain for its artistic and entertainment features. It highlights Plensa's themes of dualism, light, and water, extending the use of video technology from his prior works. Its use of water is unique among Chicago's many fountains, in that it promotes physical interaction between the public and the water.
A very nice addition to Chicago's world-renowned public art collection; fun to sit down and relax (or else bring a towel and spare clothes!).
The fountain & water jets are great for the kids to run around and frolic through, especially on a hot summer's day.
The lighting is fantastic – you should go at night to see the full effect.
Image by Teacher Traveler on Flickr under Creative Commons License.


Most hay fever sufferers have specific plants they are allergic to. The common high time for allergies is when the pollen count is high in spring and early summer. If this matches your experience, then put off your camping trip until summer or fall. On the other hand, if harvest season tends to bring on the itchy eyes and runny nose, then plan a summer trip to avoid this trigger. By simply knowing your main allergies, you can plan a more successful trip.
Your campsite can be one of the more important pieces in solving the allergy puzzle, especially if you are staying in a tent. Avoid pitching a tent near known or potential allergens, such as under a flowering tree or next to a wildflower meadow. Camping on top of a slight hill provides you with a breeze, which blows away pollen. Another option is to consider beach camping on the sand since this area is generally free of allergens.
A tent leaves you exposed to just about every allergen. If your hay fever tends to be severe, consider an alternative. Renting a cabin or taking an RV on the trip will provide a better barrier against outdoor allergens, which means you can have an oasis away from your hay fever. It's even better if the lodging has AC since this will filter out allergens in the air.
Don't plan activities that could trigger allergies. A wildflower hike is probably a bad idea if pollen sets you off. Swimming or water skiing, on the other hand, may be a better choice since few allergens survive on the water. This comes back to knowing your allergies so you can choose activities that allow you to avoid them.
Talk to an allergist or ENT specialist for more help in diagnosing and properly treating your hay fever so you can enjoy the great outdoors again.


Find your Background Singer player in Bixby Knolls here. As a proven leader in online music services, BandMix.com has the Background Singer player you are looking for in Bixby Knolls. Listen to audio samples, view pictures and video on local profiles. Browse the Listings in Bixby Knolls, California below or Search for exactly what you are looking for.


Today (Friday) was a significant date in the legislative process, though it isn't the hard deadline it once was. For both the House of Representatives and the Senate, this is the self-imposed deadline for moving bills out of the first legislative chamber. Bills that have been approved will swap chambers and begin the committee process anew. In days past, if a bill had not been considered favorably by this point it was pretty much a dead issue. Legislative leaders have become more lenient through the years and now dozens of bills will simply have their deadlines extended to retain their viability.
The Senate, originally scheduled to be in session today, cancelled and adjourned Thursday afternoon. The House wrapped up action on bills on "Third Reading" today, which was the final passage stage in the chamber. The Senate will return to the Capitol next Tuesday and the House is scheduled to return on Tuesday, May 9. The final adjournment date for the spring for both chambers is May 31st.
Click here to read the entire Alliance Legislative Report 100-16, including the bills in action this week.


text:
Multifamily Veteran Gets New Lease on Career
REAL ESTATE: Robert Hart discusses starting own firm, appeal of Class B apartments.
By Hannah Miet
For 14 years, Robert Hart was chief executive of Kennedy Wilson Multifamily Management Group. Twenty-two months ago, he founded his own investment firm in Brentwood, TruAmerica Multifamily, and has since amassed more than $1.5 billion in multifamily assets on the West Coast. In Los Angeles County, Hart's firm has acquired nearly $500 million worth of apartments in the last nine months, many of them below Class A. The Business Journal caught up with the 57-year-old Hart recently and asked him why he started out fresh and why he's interested in well-used buildings.
Question: What was your motivation for striking out on your own?
Answer: I wanted to do something that was more my own. Real estate is a cycle-oriented business, and I saw that the timing was right based on where we are in the cycle and where I am in my career with the capital partners I have garnered. Sometimes you have to take the plunge.
So you went in with a capital partner you had a relationship with?
Yes. The Guardian Life Insurance Co. of America in New York is my 80 percent partner in TruAmerica. They have an ethical, long-term view of this business, and they've been around since 1860.
It seems like you've hit the ground running. Did you expect the pace to pick up so quickly?
Candidly, when I first made the transition I was nervous about whether the phone was going to ring, even though I was a leader in the industry. When you're starting a new company, the process is the same: You put your head down, you build a track record of showing up on time, and that translates. My old boss Bill McMorrow at Kennedy Wilson always said, "You have to put your bets down." So we put our bets down.
You partnered with Capri Capital Partners to buy the Vermont, a new 464-unit high-rise in Koreatown. How much of your focus is on this kind of Class A acquisition?
Newer buildings like the Vermont represent about 25 percent of our strategy, while repositioning is about 75.
Tell us about what distinguishes TruAmerica: that 75 percent of "repositioned" apartments.
Our major focus is on what I call market-rate workforce housing. It's Class B. There is a lot of older housing stock in L.A., and there's no one building for Class B, so my focus is to find quality two- and three-story buildings that have the right bones to reposition at a higher level. We're adding better amenities, running them better, giving them better finishes and rebranding them. It's for renters by necessity, which has been my stock in trade in all the investments I have done in my career.
"Renter by necessity"?
As qualifying for mortgages has become more rigorous, families and individuals have chosen to delay homeownership. The homeownership rate in L.A. is fairly low, while the rental rate is high, and our typical renter is in the $50,000-to-$75,000 household income range. That's what's driving the multifamily market. I grew up in what I would call a tenement, and didn't own a home until I was 30. People today are living that way for longer, and it's by necessity. The Vermont, for example, is a luxury building, so that would be more for renters by choice.
Any advice for someone who is just getting started as an investor in multifamily?
My first investment was a two-unit apartment building in Venice in 1986. Since then, I've always invested side by side with my own capital. You have to put some skin in the game. You also have to be engaged with operations. You have to touch the real estate regularly to understand the property and its residents. Owning apartments is a consumer business, and at the end of the day, you're dealing with tenants who want to have a nice home.
Ares Management and TruAmerica Multifamily Acquire Arizona Apartment Building in $30 Million Deal
Koreatown Towers Sold for $283 Million
Kennedy-Wilson Acquires Long Beach Apartment Complex
Real Estate Firm to Recast Talent Agency's HQ
Vacancies Void Value of Rental Property Investments
Doctors Claim Malpractice in Condo Development
SPECIAL REPORT: Building costs
Moving Into Koreatown


High-tech means of identity theft include methods like backdoor entries, use of bots, hacking, phishing, etc. Apart from creating viruses to destroy computers and the information contained therein, many hackers focus on stealing identities. Hacking is actually not a crime and many companies hire hackers for assistance in developing software, checking the airtightness of certain concepts, and providing solutions in cases where user IDs or passwords are misused or lost. These white-collar employees who are actually trained to help computer companies can create malicious programs that can steal vital personal information from others computers and help in assuming others identities for financial benefits. Hackers can hack into the computers of large companies or retail stores and get information like name, salary, credit card details, etc. from their records. According to Symantec Corp., the makers of Norton Antivirus, more than 75% of all hackers have shifted focus from notoriety crimes to identity theft crimes.
Shakespeare, Othello, Act III, Sc. iii.
"Identity Theft Using the Knowledge of Computer." 31 March 2009. Web. 19 April 2019. <https://www.essaytown.com/subjects/paper/identity-theft-using-knowledge-computer/2606>.
"Identity Theft Using the Knowledge of Computer." Essaytown.com. March 31, 2009. Accessed April 19, 2019.


Jeanette Tamez, presenter for the University of Texas Rio Grande Valley, speaks to students about the admissions process into college during "Take a Leap to Collage Day" at Nikki Rowe High School on Wednesday, Oct. 10, 2018, in McAllen.
McALLEN — Nearly 1,500 high school seniors from throughout the local school district spent Wednesday morning learning various aspects for their transition to college, from personal finances to guidance to the numerous degrees available in the region.
The McAllen school district held the district-wide event on Wednesday for seniors as the rest of their classmates took the Pre-SAT testing.
The district invited speakers from local banks to lead presentations about managing credit, as well as representatives from area colleges and universities who went over college application requirements and degree pathways available to students.
"Our data shows an increase in the percentages of students who have submitted an application to at least one university and also the percentage of students completing their financial aid application," said Rosalba De Hoyos, school district director for college, career and counseling.
Two years ago the timeline to begin applying for financial aid moved back from the beginning of the year, Jan.1, to early fall semester, on Oct. 1, allowing students to get started in the process earlier. This also prompted school districts to shift their college readiness initiatives to earlier in the school.
At McAllen ISD, the district set up a December internal deadline to have 100 percent of students intending to go to college with at least one completed admission application, and at least 60 percent of students completing financial aid applications.
And ever since this push to start the process earlier began, De Hoyos said they have noticed the number of student applicants increasing.
Now, even with these deadlines in mind, De Hoyos said that each campus is held to their own capabilities and encouraged to not wait for the deadline but to focus on getting each student well underway and throughout the first part of the fall semester.
The district started the school year with a College Night as an informational resource for parents and students to learn about scholarships, financial aid and college admissions. Also starting this month the district is holding weekly sessions in which parents and students can set up appointments at the College Center to get help completing their financial aid applications.
The centers are available at every high school, De Hoyos said, and their goal is to get the applications done ahead of the college admissions deadlines, which for some institutions are as early as Nov. 1.
A financial aid information session open for families district-wide is scheduled Oct. 25 at McAllen High School from 6 to 7 p.m. This will be followed by a community-wide event called Transmittal Night held at South Texas College in which parents and students will receive help filing their financial aid applications from 5:30 to 8 p.m. Oct. 30.
These events, and most of the district-wide push to increase applications, would not be possible without the availability of the College Centers and the help from organizations such as AmeriCorps, an organization that places college student volunteers in area high schools to aid students in the college application process, De Hoyos said.


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Lawrence Cunningham Talks AKO Capital, Buffett, New Book In ValueWalk Interview
Lawrence Cunningham is widely regarded as an expert on Warren Buffett and his book The Essays of Warren Buffett: Lessons for Corporate America, which is now in its fourth edition, has been labeled by some as one of the top investment books of all time.
Over his career, Lawrence Cunningham has authored more than a dozen books on Berkshire, Buffett, value investing and finance. His new book, Quality Investing: Owning the Best Companies for the Long Term outlines the investment philosophy of London-based hedge fund AKO Capital, and the lessons its portfolio managers, Torkell T. Eide and Patrick Hargreaves have learned over the years.
Ahead of the book?s release at the beginning of next year, Lawrence was kind enough to answer some questions for ValueWalk about Quality Investing, and some of the lessons learned from working with Warren Buffett over the years.
An interview with Lawrence Cunningham
Rupert Hargreaves: You?ve got a new book out, Quality Investing: Owning the Best Companies for the Long Term, co-authored with Torkell T. Eide and Patrick Hargreaves. Could you give our readers a brief summary of the book?
Lawrence Cunningham: Quality Investing outlines the investment philosophy of AKO Capital, and the lessons its portfolio managers, my co-authors, have learned. AKO, a London-based fund with $10 billion AUM, has achieved a return of more than double the market (9.4% per annum versus Europe?s 3.9%) since inception a decade ago. In the book, we discern the patterns that can make quality companies?those with durable competitive advantages?worth paying up for.
RH: What was the inspiration behind the book?
LC: AKO initially wanted to explain their philosophy and stock selections to clients, predominantly large U.S. university endowments with an inherent long-term horizon and a taste for quality. Knowing my philosophy meshed with theirs, AKO recruited me to help turn the data and research into a monograph. As we refined the product, it became obvious that a wider audience would be interested. So we transformed what began as an internal research project into an investment book for a broad investor audience.
RH: What are the qualities investors should be looking for when trying to identify the best companies?
LC: Go behind the obvious. Yes, look first for economic attributes: predictable cash flow generation, high returns on equity capital, and attractive growth opportunities. But then dig deeper to discern their causes and evaluate their durability. Patterns to look for: recurring revenue streams such as under service contracts; friendly middlemen who direct consumers to a producer?s output; and producers catering to customers who pay a premium whether due to brand strength or other intangibles that provide pricing power.
RH: Do you have any examples?
LC: We give more than 20 case studies, including global titans like Diageo, Hermès, L?Oréal, and Unilever?as well as lesser-known companies in different industries from agriculture to plumbing and banking. We also present examples of failed investment decisions, such as Tesco and Saipem. The companies analyzed in the case studies are exemplars of the patterns that we describe. For example, elevator manufacturers such as Kone illustrate the pattern of recurring revenue from service contracts. Geberit, Europe?s leading maker of lavatory products such as toilet flushing systems and pipes, has cultivated deep, loyal relationships with plumbers who routinely recommend its products to customers as ?friendly middlemen?. Hermès and L?Oréal are assessed as paradigms of companies that turn intangible customer benefits into pricing power. In addition, we explore the value of assurance offered by product testing companies such as SGS and Intertek; the appeal of technological advantages at Syngenta, the global market leader in crop protection; and the moat of research-led innovation at Novo Nordisk, which dominates the global insulin market.
RH: What do you hope readers will take away from the book?
LC: Three things: First, a company that can deploy capital at high incremental rates of return over a long time-frame will deliver significant compound growth in earnings. Second, the combination of competitive advantages that allows a company to do this are rare ? but there are common patterns that tend to indicate sustainable quality. Third, while quality companies do attract a valuation premium, these premiums tends to under-value the long-term compound growth. Quality companies are worth paying a premium for, even for a traditional value investor.
RH: There?s no doubt that markets are becoming increasingly focused on short-term returns despite the overwhelming amount of evidence which shows that investing for the long-term always yields the best results. What advice would you give to investors who are struggling to ignore short-term market fluctuations?
LC: Focus on the fundamentals. Are share price moves reflecting a change in underlying thesis? If not, dislocations should be seen as an opportunity rather than a negative. Focus on costs. Responses to short-term fluctuations entail the frictional costs of trading, taxes, and reinvestment risk?as well as the emotional and psychic costs of worry. Read history. Look back over decade upon decade and observe that upheavals recur but have all been overcome. Think about the position you want to be in ten, twenty, and thirty years from now.
RH: You?ve done a lot of work with Warren Buffett in the past, is Quality Investing an explainer of Buffett?s investment style, or does it draw from the lessons of business leaders around the world?
LC: The AKO philosophy draws on Buffett and others, especially Phil Fisher and Charlie Munger. In a sense, it reflects Buffett?s evolution from a pure or deep value investor to one who is willing to pay up for quality. In that way, the quality investing philosophy takes value investing one step further, by focusing far more on quality than price. Consider what Tom Russo said about Quality Investing when recommending this book: ?It shows why the best long-term margin of safety comes not from an investment?s price but from the value of a company?s competitive advantage.?
RH: One of your previous books, The Essays of Warren Buffett: Lessons for Corporate America has been labeled by some as one of the top investment books of all time. As you reflect back on the book, which is in its fourth edition, is there a lesson or two that really resonates with you as an investor or a student of business?
LC: Long-term, disciplined agility pays. Berkshire has had only one CEO for 50 years. He has lived by a short list of fundamental values while endlessly adapting to changing environments. He has varied his investing style and forged dramatic change in the asset mix of the business, from one dominated by
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Home › War Memorials – St. Mary's › Stone Cross › Memorial to the Fallen
Memorial to the Fallen
From the Burton Chronicle, 19 December 1918:
The first meeting of the above Committee concerned with this matter was held at the Oddfellows Hall on Monday evening when Major H L Newton, DSO, presided. A collection committee was appointed and a district allocated to each collector. The following sub-committee were chosen to estimate the appropriate cost, to select the site and to decide on the form of memorial:
Major H L Newton, Chairman
Mrs H Newton
Mrs S Walker
Rev H Bennett
Rev J Charles
Mr F A Ashton (?)
Mr G L White
Mr S A Lester as secretary.
It was mentioned that the number of fallen from the parish was 42 and already a donation of £20 had been promised.
Bodley & Hare (Gray's Inn Square, London – 1907-1942) were chosen as the architects (specialists in War Memorials and ecclesiastical modifications). The War Memorial was designed by Cecil Hare, executed by Bridgemans of Lichfield and dated as 1920, a Renaissance design[1].
The description of the War Memorial on the War Memorials Archive (Imperial War Museum) is'Foliated sandstone cross on six faceted tapering pillar. Shields in relief. Three figures: female angel, St. George and St. Michael.'
In the Lady Chapel there is a framed architectural drawing of the Memorial, resting on the floor, which will need conservation and protection. Click on the picture below for a larger view.
The Burton Chronicle of 6th May 1920 recorded the dedication of the War Memorial:
Dedication of War Memorials
A large congregation assembled at the Parish Church on Sunday[2] afternoon to witness the dedication of the war memorials. The first part of the service was held in the church, where the Bishop of Stafford dedicated a beautiful stained glass window, erected in the south aisle, and a tablet containing the names of the forty-seven Tutbury men who fell in the great war.
The congregation then moved into the churchyard, where another large assembly had gathered on the southern side of the church to witness the dedication of the new parish memorial, a handsome stone cross beautifully carved in Hollington[3] stone by Messrs. Bridgeman and Son, of Lichfield to the design of Mesrts. {sic} Bodley and Hare of London. Amongst those waiting were a large company of ex-soldiers, members, of the local branch of N.F.D and D.S. and S.[4] who were in charge of Captain B. Newton; members of the parish council; the police; the scholars and teachers of the Wesleyan S.S.
The ex-Service men had paraded from the town to the church, and had formed up on the bacv {sic} ground of the site, making a most impressive scene. Preceding the Bishop from the church were the choir, the churchwardens (Messrs. W.E. Guest and D. Rutherfoord) while the vicar of Tutbury (the Rev. H. Bennett) undertook the duties of chaplain. Most impressive was the singing of "O God, our help in ages past," the first hymn sung outside.
The Rev. J. Charles, pastor of the Congregational church, read the lesson, which was taken from Wisdom iii., and the following names of the fallen were read: William E. Priestly, John J. Kidd, Ernest Woodall, Ernest Plumb, Robert Radford, William Gent, Charles Bull, James Worrall, George E. Bentley, Wilfren {sic} Powell, Frederick Bannister, W. Trafford Newton, George F. Bennett, Charles Bond, Tom Merrey, Thomas Coates, James Nash, Arthur Treadwell, Edwin Powell, William Cook, Joseph Talbot, William Bentley, Frederick Bennett, Samuel Woodal {sic}, George Causer, Abraham Farmer, Eric Dunicliffe, Stephen Leedham, William Hinds, James Hand, William Smith, Edwin Broughton, William Wheeldon, Albert Timmins, Ernest A. Perkins, George Fearn, Joseph Pegg, Jack Leadbetter, Alfred Brettell, Samuel Wheat, William H. Parker, Harry Allsop, David Bond, Frederick Smith, Ogden Pateman, E. Frank Hadland, Alfred J. Parker.
The ceremony of the unveiling of the memorial was then performed by Major H. L. Newton, D.S.O. Under the figure of Peace, a bronze tablet was shown, bearing the inscription, "In grateful memory of Tutbury men who gave their lives in their country's defence, 1914-1918," and under the figures of St. Michael and St. George were shown the tablets bearing the names of the fallen. In an impressive manner the "Last Post" was sounded by Mr. Harry Bell, ex-sergeant K.R.R[5].
After the Bishop had dedicated the memorial, the reveille was sounded by Mr. Bell, followed by the singing of "The King of Love my Shpeherd {sic} is." From the text "God forbid that I should glory save in the cross of Jesus Christ." his Lordship gave a splendid address, in which he spoke most touchingly of the noble sacrifice of the men. He asked his hearers to ever remember their brave deeds, and spoke of the value of memorials to keep these men and their unselfish acts fresh in our memories. The Hymn "They whose course is o'er" was then sung, and after the Bishop had pronounced the Benediction, the National Anthem was sung as a conclusion to the service. The singing for the service was lead by the choir, under Mr. C. Owen, organist and choirmaster.
A large number of floral tributes was placed on the memorial at the conclusion and on the front was placed a large laurel wreath. Amongst those who placed tributes were relatives and friends of the fallen.
We have not been able to find any record of the criteria for inclusion of men on the Memorial and some men do appear on more than one local War Memorial, showing the close nature of villages in this area.
The names appear to be recorded on the Memorial in order of the date of their death and no rank, unit or decorations are shown, symbolising the equality of sacrifice of all casualties. This is in keeping with the edict from the first meeting of the (then) Imperial War Graves Commission in 1917 which decreed that 'in the erection of memorials and graves there should be no distinction between officers and men'; further it was agreed that there should be 'no distinction between creed and nationality'. Both of these were radical departures from what had gone before.
[1] Pamphlet "The Priory Church of St. Mary the Virgin, Tutbury, Staffordshire" (Author T.J. Ganz, Vicar).
[2] The dedication was on Sunday 2nd May 1920
[3] Hollington Stone: available in Red, Mottled, Cream from Hollington, Staffordshire ST10 4HQ
[4] N.F.D and D.S and S.: The National Federation of Discharged and Demobilized Sailors and Soldiers was founded in January 1917 by London-based veterans opposed to the Review of Exceptions Act by which men invalided out of the armed forces could be re-conscripted. It adopted the slogans "Every man once before any man twice". Senior military figures refused to attend its meetings, and the Federation's politics became broadly liberal. The Earl of Derby founded "Comrades of the Great War" as a right-wing alternative. In 1920 the Federation invited the National Association of Discharged Sailors and Soldiers (NADSS), "Comrades of the Great War" and the Officers' Association to a meeting to discuss merging, and this was achieved in 1921, establishing the Royal British Legion. {Source:www.en.wikipedia.org/wiki/National_Federation_of_Discharged_and_Demobilized_Sailors_and_Soldiers }
[5] K.R.R: King's Royal Rifle Corps
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Jane Philips Studio
Throughout my life, I have struggled to varying degrees with an anxiety disorder. It's as if I share my mind with a dark tide. At times, it swells unbearably to the surface, threatening to drown me. At other times, it remains a soft lull in the corner of my mind, waves peacefully lapping at the shore, almost forgotten.
Much of my art explores these feelings of anxiety. My figures dwell in dark, undefined, swirling masses that transform into mental landscapes. At times, the background obscures the figure, overwhelming it. At other times, it retreats, just as the tides of my own struggle ebb and flow. The glow of gold is a warm comfort within the darkness and a soft recollection of religious iconography that creates saints of the unabashed, naked curves of feminine forms.
In growing and exploring my identity as a woman, I've struggled to break with the ideas of traditional femininity: that to be feminine is to be small and unseen. Through my work, I depict women in a realistic and unidealized way that gives them power over their own expression,identity, and body.
In the grand tradition of figurative art, my figures are painted in oil. Most of my subjects are nude. Nudity expresses vulnerability as well as ownership of one's own being. For eons, male artists have used female bodies as subject and muse. In fact, my figures are often described as "Rubenesque" as if the artist himself invented full bodies and natural curves. It is important for me to reclaim this imagery for myself and for the women I depict. I think women in art,both as artist and subject, have not held their own agency in history. I want to show women in a way that is personal and genuine.There is frailty and strength; sexuality and stillness; so many contradictions wrapped into a single form that express the many facets of femininity.
Many of my works evoke the flora and fauna of the American South. Grasses, lily pads, snakes, frogs, and algae recall the disquieting and beautiful habitat of the swamp, ever circulating with death and growth. The detritus creates haunting gloominess and beauty, the remnants of life nurturing new life. The brown hues recall dirt,from which all things grow and return.
More frequently, my figures are captured in scenes of nature, surrounded by Queen Anne's Lace, forests, and hiking paths. In my own life, I seek connection with nature to find solace and to calm my mind; to reflect in solitude. Many times, my paintings depict a solitary figure, the story of a singular being reflecting in nature and connecting with the universe and their truest self.
My art has allowed me to express myself and to expel my inner demons. My vocabulary has evolved through experimentation with different techniques and modifying traditional mediums. The results are at once sedentary and dynamic. The oil painted figures are often rendered in silent meditation. The environment is created through physical motion; I climb atop and around the canvas, flinging paint, throwing ink, and sprinkling dust. The canvas becomes a punching bag; I sling,hit, slap, and beat coffee, paint, ink, and charcoal into the surface. I experiment with different materials in order to create chemical reactions, and seek to immobilize them beneath a glossy surface. These reactions are often created by attempting to combine opposing ingredients such as oil and water, ink and turpentine. Many of the brown hues are created with coffee.
My work is a way for me to reflect on my own life and struggles and express it in a way that other people can connect with. There is such beauty in skin; in the translucency of flesh and the map of bones and muscles underneath; the reflection of light from divots and creases and hidden places. There is vulnerability and openness in nakedness. Our bodies tell stories. We identify with others, existing in vessels that are simultaneously endlessly diverse and familiar. We recognize ourselves in each other.


Lifestyle and health factors that are good for your heart can also prevent diabetes, according to a new study by researchers at The Ohio State University College of Medicine that published yesterday in Diabetologia, the journal of the European Association for the Study of Diabetes.
Diabetes is a growing problem in the United States, with nearly a third of the population living with diabetes or prediabetes, according to the Centers for Disease Control.
Joshua Joseph, MD, an endocrinologist and assistant professor at The Ohio State University Wexner Medical Center, wants to bring those numbers down. He studies various ways to prevent diabetes. His latest work looked at how cardiovascular health can impact diabetes risk.
The Life's Simple 7 health factors and lifestyle behaviors that are associated with cardiovascular health are physical activity, diet, weight, cholesterol, blood pressure, blood glucose, and tobacco use.


Update: If you prefer digital to print, the fine folks at Comixology are having a 50% off sale on all IDW books, including Augusta.
As anyone who has followed this site for more than five minutes knows, no musician has impacted my life more powerfully, and more permanently, than John Lennon. His extraordinary songs—which form the greatest musical autobiography in rock and roll—shine on, speaking to our world today as powerfully as they did all those years ago. Happy birthday, John.
A bit of a melange today, some odds and ends I want to bring to your attention. First, this past Wednesday saw the release of a brand-new edition of one of my favorite projects, The Last One. Originally done for Vertigo, back in the 90's, this new hardcover comes from the fine folks at IDW. The story deals with Myrwann, an immortal being living in New York's East Village, where he (or is it she?) has taken a group of lost souls under her (or is it his?) wing. The art is by the amazing Dan Sweetman (best known to comics fans for his work on Beautiful Stories for Ugly Children) and I'm delighted that this is finally back in print.
Next, a gentle reminder that the second issue of The Adventures of Augusta Wind: The Last Story is on sale now. I loved working on the first Augusta series with my wildly-talented collaborator, Vassilis Gogtzilas, but this second series, which wraps up Augusta's epic story, has been one of the most exhilarating creative experiences I've had in years. It's hard for originals like this to make their way across an ocean filled with Marvel and DC relaunches and licensed books based on hit movies and TV shows, so I'd love it if folks could spread the word about this series. If you enjoyed Moonshadow or Abadazad (to name two), I think you'll enjoy this.
My final convention appearance of the year is coming up in November, in Bangalore, India. It's been eleven years since my last trip to that magical country and this one promises to be very special, on so many levels. Really looking forward to it.
And, finally, let me point you toward the Hollywood Reporter, where they list their choices for "The 100 Greatest Superhero Comics." Both Justice League International and Kraven's Last Hunt made the list and, however arbitrary these things can seem, it always feels good to be remembered and appreciated. One of the things I liked about this particular list is that it includes some unexpected, and worthy, choices. Check it out.
This concludes today's gallimaufry. To paraphrase The Outer Limits, we now return control of your computers to you.


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Prepare for the Long Haul Part 3: Avoiding Legal Liability
POSTED ON February 12, 2019 / IN Documentary Toolkit
STEVEN C. BEER, JAKE LEVY AND NEIL J. ROSINI
This Q&A was originally published in the Fall 2018 issue of Documentary magazine, a publication of the International Documentary Association, a nonprofit media arts organization based in Los Angeles.
Apart from steering around legal potholes relating to business structure and intellectual property ownership, it's generally good advice for a producer who wishes to continue for the long haul to avoid lawsuits, either as a plaintiff or defendant.
Perhaps the best advice for a producer in this regard is to choose with care those with whom one does business. If the other party to a deal has a poor reputation for integrity, there may be a good reason.
Another good way to avoid lawsuits is to understand the terms of contracts. A producer who knows exactly what a contract yields or withholds is more likely to avoid the courtroom than one who doesn't. This maxim applies on the giving side: What is the producer committing to do? Is the cash flow sufficient? Are the obligations reasonable? But it also applies on the receiving end: Is the performance expected of the other party reasonable and feasible? If contractual terms like "warranty," "indemnification," "holdback" or other customary industry terms are unfamiliar, the prudent producer will seek advice before signing.
Obviously, lawsuit avoidance should also focus on the content of the production. Will it defame anyone by injuring reputation or invade privacy or publicity rights? Does it unlawfully capture a performance? Does it infringe copyright either by borrowing clips, music, photos or other third-party property? If fair use is claimed, does the production meet the strict criteria that apply to that defense? Although it's a good idea to be familiar with legal standards that apply to these risk areas, obtaining a legal review is the most practical approach; in any event, that review is required for errors and omissions insurance. This coverage is not only a good way to reduce exposure to the financial consequences of inadvertent defamation and infringement of third-party intellectual property rights, but it's also a necessary deliverable for deals with distributors, exhibitors and platforms.
Other forms of insurance are also either advisable or necessary or both. General liability insurance, for example, protects a production against property damage and bodily injury to third parties who aren't working on the production. Workman's compensation insurance covers bodily injury to cast and crew.
Insurance isn't inexpensive, but it can avoid ruin, which is the opposite of sustainability.


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Home > Business & Events > Company News > Xicato Doubles Its Space to Accommodate New LED Light Module Manufacturing Line in Silicon Valley
News-Spot | Business News | Jan 10, 2014
Xicato Doubles Its Space to Accommodate New LED Light Module Manufacturing Line in Silicon Valley
Xicato, a leading developer of superior light quality from LED modules, announced today that it has relocated its San Jose headquarters to accommodate a new manufacturing line for the company's next generation of products. The new 24 thousand-square-foot space is more than double the size of Xicato's previous San Jose facility. The privately held company has invested millions of dollars in equipment and resources to meet the increasing global demand for its LED modules.
"Investment in our U.S. operations enables Xicato to rapidly develop and introduce innovative technologies and products for the global lighting industry and starting next month we will transition to full manufacturing mode," said Menko de Roos, CEO of Xicato. "Our move timeframe was very short, and it was critical that the build-out and permitting went smoothly and matched our equipment arrival schedule. The City of San Jose's appointment-based permitting process worked well and allowed us to meet critical milestones and get our operations up and running."
Xicato's new space includes areas for manufacturing, research and development labs and testing facilities for its LED modules. Class 100K cleanroom space is dedicated to a new manufacturing line; expanded reliability testing labs ensure product longevity and quality.
Xicato's focus continues to be the delivery of quality light. The new manufacturing facility allows Xicato's next generation of products to offer significantly improved efficiency to reduce energy use and costs, lowers the cost of Xicato's modules and ushers in a new era of intelligent lighting - Lighting 2.0.
Hundreds of luminaires and applications ranging from retail shops to bars and restaurants, to homes, offices and retail locations can be viewed in Xicato's web galleries on the company's website. Lighting designers, interior designers and others achieve their desired emotional impact using three different light types created by the company.
• Standard Series light provides a smooth spectrum of light that feels natural and superior to ceramic metal halide and fluorescent lamps..
• Artist Series light is indistinguishable from halogen-based light and gives Artist Series light an even richer feel compared to other sources.
• Vibrant Series light brings out vivid, saturated colors and the details of texture for compelling and eye-catching visuals that capture and hold consumers' attention.
Xicato's new address is 101 Daggett Drive, San Jose, CA 95134, USA.
About Xicato:
Xicato is changing the way the world sees and experiences light. We believe that with better light, shoppers will buy more, diners will order more and guests will be more comfortable and happier with their experiences. In turn, better lighting will contribute to higher sales, margins and brand value. Xicato has engineered new sources of light, Standard, Artist and Vibrant that capture the human preference for experiencing light in different ways and extend the range of choices for the lighting design community. The true value of Xicato light lies in delivering on the aesthetic and economic potential of LEDs: the quality of light, its efficacy and ownership cost for all environments including retail, hotels, restaurants, bars, museums and residences.


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Frimley Green traffic lights: Residents still waiting for council meeting more than a year and-a-half after petition demanding review of four-way system
Frimley Green residents created an online petition opposing the planned four-way traffic lights in November 2015
Isabel DobinsonReporter
"The village green is at the heart of the village," says campaigner, Graham O'Connell (Image: Graham O'Connell)
After more than a year and-a-half of campaigning, "angry" and "frustrated" residents demanding a review of traffic options in Frimley Green are still waiting for a meeting with council officials.
A four-way traffic lights has been proposed to replace a double roundabout at the junctions of Frimley Green Road, Wharf Road, Guildford Road and Sturt Road.
Part of the ongoing development of Deepcut 's Royal Princess Barracks (PRB) to build 1,200 homes, the plan has raised concerns over potential congestion and rat-running.
Traffic lights dispute
Protests continue
Demands for review
Should plans go ahead?
Lights will make traffic worse
Lead campaigner Cliff Hilton said: "It is 21 months since we submitted out petition to the council.
"Following that we presented our case in a comprehensive written report to Surrey Heath Borough Council (SHBC) last September and we still have not yet had a response nor even a date to meet."
He added: "We had a sympathetic hearing at the Surrey Heath Local Committee meeting on July 13 but we still want to meet with the key players to discuss the options for a modified scheme before events overtake us and wok on the lights gets underway."
"It is 21 months since we submitted out petition to the council," said campaigner, Cliff Hilton
Local residents say they were not consulted over the proposed four-way lights, which are part of traffic mitigation plans.
Campaigner Graham O'Connell said: "The village green is at the heart of the village.
"It is a designated green space and is common land, yet they want to put traffic lights on it.
"We want to work with the council to get the best solution for the village."
Deepcut Barracks development: Protests continue over Frimley Green traffic lights which 'will change village life forever'
A meeting was scheduled for June but was cancelled due to the General Election - ward councillor for Deepcut and Surrey County Councillor (SCC) Paul Deach has now taken on the responsibility to organise a meeting.
He told Get Surrey : "Residents feel these measures are inappropriate and will not only make the traffic problems worse but also cause rat running through the green and other side streets - I am inclined to agree."
A map of the junction, where the proposed traffic lights will be implemented (Image: Graham O'Connell and Cliff Hilton)
"I am in the process of arranging a meeting which we hope to have soon.
"The aim is for officers from the borough council who are the planning authority that gave consent for the PRB development and officers from the county council who are the highway authority to meet with residents, elected representatives, including Michael Gove, the SCC highways cabinet member, Colin Kemp and myself to look for a pragmatic way forward."
He added: "It is local residents that will have to live with the possible consequences of highway works from PRB, it is inly right that relevant parties discuss and find solutions to minimise any impact from out community."
Surrey Heath headlines
Camberley traveller incursion
Gove's keynote speech
Fairoaks Airport update
Gove's view on Longcross
The online petition opposing the traffic lights was created in November 2015 and has since received more than 1,000 signatures.
According to Mr O'Connell and Mr Hilton, this is "extraordinarily high for such a small village and is testament to the volume and strength of feeling amongst residents."
To see the Mr Hilton and Mr O'Connell's traffic report, click here .
Keep up to date with the latest news from around the county via the free Get Surrey app.
You can set up your app to see all the latest news and events from your area, plus receive push notifications for breaking news.
Available to download from the App Store or Google Play for Android .
Princess Royal Barracks