Data teams are very hard to hire. Engineers are low in supply and high in demand, resumés are difficult to vet without time-consuming phone/coding screens, and often without FAANG's resources it can be hard to find people as passionate as you are about your product. As the hiring manager, you may be lucky enough to afford the services of a professional recruiter. However, often the recruiters send you completely unsuitable candidates.
A very friendly recruiter I know in NYC and I had coffee the other day to discuss these industry issues. We agreed that it would be useful if we defined what makes up a great data team and what the roles actually mean.
Who should I hire first? And how do they work together?
Your goal in hiring a data team is probably business insights and getting to a point at which your actions are largely data (rather than intuition) driven. For this, a competent Data Analyst is your starting point. Thankfully, Data Analysts are in plentiful supply. New grads with a passion for digging for insights and a quantitative or semi-quantitative (e.g. Econ) background shouldn't be too tough to find. On the high end you can find those with experience in SQL and also relatively new BI tools such as Looker. If you have the budget, 75% of the engineering tasks that they will encounter can be solved with plug-and-play pipeline tools such as Segment. Most standard business insights – "what happened yesterday/last month?", "what's an underserved demographic in my customer base?" can be answered without an expensive Data Scientist.
At some point, your databases and queries are going to become too slow. You'll also want to scrape a data source or use an API that no-one has built a connector for in Segment. Perhaps you'll want some custom lead scoring for sales, there'll be a bunch of security issues or there's some giant set of queries that you want to have refreshed every morning and notify you in Slack. Not having an programming background, your Data Analyst will feel out of their depth. This is when you need to hire a Data Engineer. Their expertise really lies around getting your datasets to the point at which they can be analyzed. If your tables are getting 50 million events per month then queries will start to take hours overnight and tables may even lock up. The Data Engineer can alleviate this – they will be able to optimize the indexes of your database's tables for fast lookups, create materialized views of helpful aggregates that refresh every morning, connect custom APIs to your data warehouse and generally go above and beyond that which is covered by plug-and-play tools.
Now that you're getting standard business insights, and getting them at scale from interesting sources you're going to almost certainly be curious about the siren song of AI. What if you could predict which customer will churn/convert? What if you could cluster your customer base by behavior? This sort of next-level insight is all possible with a competent Data Scientist. They will use Machine Learning libraries in Python to try to automatically classify your customers and actions in your system. They can predict the future (with certain degrees of accuracy) and tell you stories about your data. This will involve using the large datasets that the Data Engineer has provided. Once these insights have been surfaced, they will have a trained model which can be saved to disk and refreshed. But what if this needs to be refreshed on a daily basis, and those insights added to a table that the Data Analyst or even Salesperson can read? Then you need to go back to the Data Engineer and have them set up a backend system to do this.
Sometimes, your (possibly more old-school) Data Engineer will be 100% pipeline and database admin focused and not particularly experienced at implementing the Python-based models your Data Scientist came up with. For this issue, you need a Data Science Engineer or Machine Learning Engineer. Their expertise is in deploying, scaling and refreshing the model that the Data Scientist came up with. The Data Scientist should be focussing entirely on probabilities, tweaking model parameters and confidence scores; your Data Analyst should be focussing on the higher level narrative, and your DS/ML Engineer can now take the model and make sure it delivers your insights quickly, and with clean, fresh, correct data.
What if Looker doesn't have the charts you want? What if you need to visualize a network? What if your data has 16 dimensions and you've exhausted all the color, size and shape options in your scatter plot? Then you need the very specialized role of Visualization Engineer to build you custom visualizations and think of better ways to surface the insights that the Data Analyst struggled with.
This is an ambiguous title – they could be a Data Science Engineer, or Data Engineer.
But why does everyone just focus on Data Science?
Data Science is a buzzword that some use to represent anyone who fits in the intersection of Data Engineering and Analytics. One of the biggest mistakes companies make is in hiring too many Data Scientists and not enough Data Engineers. Data Scientists are getting easier to hire recently as many former Mathematicians, Chemists, Statisticians and other quantitative grads have found they can either easily rebrand themselves or attend a 3-month Bootcamp and get hired in a junior role. However, their models and their insights can be limited as they don't know how to scale/deploy them; and they will spend much of their time cleaning the datasets rather than deeply concentrating on the message and predictions hidden within. Of course, as someone who mostly now focuses on Data Engineering I am biased, but I would say that each Data Scientist should be paired with at least 1 Data Engineer. The reason I actually moved into Data Engineering from Data Science was really out of necessity for my own work.
I hope this guide was successful in explaining today's data teams. Please comment or email me if you have any ideas about how to improve it.


text:
\section*{Acknowledgement}

This work was partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2070 – 390732324.
Other parts were funded by the Federal Ministry of Education and Research of Germany as part of the competence center for machine learning ML2R (01IS18038B).

\section{Introduction}\label{chap:intro}

In a recent study, the Food and Agriculture Organization of the United Nations stated that almost \num{1.4} billion hectares or \SI{30}{\percent} of the world's agricultural land are used to produce food that later goes to waste. They also stated that global food loss causes \num{3.3} gigatons of carbon dioxide emissions or \SI{7}{\percent} of the world's total greenhouse gas emissions~\cite{fao_foodwaste_report, fao_sofa_2019}. Efficient use of agricultural and logistical capacities is therefore crucial for sustainable global food supply.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.75\columnwidth]{images/Problem_sketch.pdf}
	\caption{Problem setting: for given planting dates, corresponding harvest dates have to be predicted. Predictions are based on uncertain Growing Degree Unit (GDU) forecasts. The challenge is to schedule planting dates for more than \num{1000} individual crops and desired yields simultaneously, resulting in a continuous harvest yield distribution.}
	\label{fig:problem_sketch}
\end{figure}

A particular application of artificial intelligence in agriculture is the optimization of harvest schedules or agri-food supply chains~\cite{survey_harvest_optimization}. Even in other food-related disciplines, harvest schedules are of major importance, e.g. in aquaculture~\cite{harvest_scheduling_aquaculture}. In this work, we will focus on optimizing a planting schedule with regard to a sustainable yield sketched in Figure~\ref{fig:problem_sketch}. In particular, our goal is to find planting dates for many crop species such that the resulting yield is efficiently distributed. In this way, we facilitate prevention of food waste and surplus greenhouse gas emissions.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.75\columnwidth]{images/GDU_GP_forecast_all.pdf}
	\caption{GDU forecast mean and $\pm 1\sigma$ standard deviation for site 0 (blue) and site 1 (orange).}
	\label{fig:gdu_forecast}
\end{figure}

Scheduling problems are difficult to solve by involving numerous soft- and hard constraints and requiring to factor in environmental conditions. Common approaches thus consider stochastic models and commercial solvers which, however, are essentially \enquote{black box} systems whose decisions are opaque and difficult to comprehend. Alternatively, scheduling problems are commonly cast as multi- or many-objective optimization~\cite{moo_book} problems. Besides many other optimization strategies like in \cite{hs-opt_winegrapes} and \cite{hs-opt_multiobjective2}, these problems are often optimized by evolutionary algorithms (EAs) like NSGA-II~\cite{nsga2} or MOEA/D~\cite{moead} as in \cite{hs-opt_multiobjective1}, or in combination with other models \cite{hs-opt_bilevel_ea}.

In the work at hand, we consider a basic evolution strategy (ES) often referred to as $(1+1)$-ES. It is simple to implement and can be generalized towards $(\mu+\lambda)$-ES which allow for distributed computation.

Our contributions can be summarized as follows:
\begin{itemize}
    \item we propose a generic formalization of the harvest schedule optimization problem, which is agnostic with respect to time scales, crop types, and desired yield
    \item a novel hierarchical loss function is devised that converts naive multi-objective problems into a single-objective problem, enabling faster optimization
    \item uniform escape from local optima is facilitated via a new dynamic oscillating mutation rate
    \item we provide an experimental evaluation on realistic data
\end{itemize}

This work provides a summary of results for the above points. A detailed discussion of methods and the full set of results will be available in an extended version of this manuscript.

\section{Background}\label{chap:notation_background}

\begin{figure*}[!htb]
	\centering
	    \includegraphics[width=0.3\textwidth]{images/harvest_site1_sc1_run01.pdf}
	\hfill
		\includegraphics[width=0.3\textwidth]{images/harvest_site1_sc2_run00.pdf}
    \hfill
    	\includegraphics[width=0.3\textwidth]{images/harvest_site1_sc3_run00.pdf}
	\caption{Weekly harvest quantity for all 3 scenarios on site 1. The bars show the harvest per week for an unoptimized schedule given in the dataset (orange) and our results (blue). The dashed lines mark target (red) and maximally needed capacity (purple). For uncertainty estimation (error bars), \num{100} harvest matrices are resampled from the GDU forecast. The over- and undershoot reduction ratios $R_o$ and $R_u$ refer to the unoptimized schedule. It is visible that our optimized schedule largely prevents over- and undershoot.}
	\label{fig:harvest_results}
\end{figure*}

The first building block of our method is Gaussian process regression (GPR) for time series prediction \cite{gp_rasmussen}. GPR is a kernel-based non-parametric method to inter- and extrapolate data points. It allows us to include available background knowledge about agricultural time series, e.g. the existence of annual periodicity, other seasonal effects, and trends, by choosing appropriate kernel functions.

The second building block are evolutionary algorithms (EAs) which are a class of meta-heuristic optimization algorithms inspired by biological mechanisms \cite{ea_book1, ea_book2}. The common procedure is to generate one or more possible solutions in the form of state vectors, called \textit{population}. During a \textit{generation}, the populations, or \textit{parents}, are \textit{mutated}, resulting in \textit{children}. An \textit{environment} thereafter has to select the best population(s) which themselves are initial states for further mutations in the upcoming generation \cite{ga_parameter_review}. This procedure within a generation cycle is referred to as \textit{evolution strategy} (ES). We focus on $(\mu +\lambda)$-ES \cite{es_overview}. $\mu$ is the number of parents that undergo mutations resulting in $\lambda$ children. The \enquote{$+$} sign indicates that, when selecting the $\mu$ \enquote{fittest} mutants, parents \textit{and} children are taken into account. Since EAs are meta-heuristic optimization methods, they typically yield only approximately optimal solutions to a problem. The major advantage, however, is that they can handle discrete high-dimensional and multi-objective inputs and that their objective- or \textit{fitness} function does not have to be (continuously) differentiable as in the case of, say, gradient descent based methods.

\section{Modeling the Problem}\label{chap:methodology}
Next, we focus on our harvest scheduling problem by discussing constraints, the mathematical problem formalization, and adaptations to the optimization algorithm, i.e. oscillating mutation rate and the hierarchical loss.

\subsection{Constraints}\label{sec:constraints}
Seeding and growing of crop are fraught with several constraints and uncertainties. Two constraints are the windows or periods of possible planting and their planting as a whole at one point in time for each individual crop. Furthermore, we introduce the two variables, i.e. desired yield for each species and the \enquote{performance} of the field to accumulate crop growth. In phenology, the latter is often measured by \textit{Growing Degree Units}. This is the accumulated time-dependent temperature $T(t)$ above a certain base temperature $T_\text{base}$ during the interval of interest. Hence, $GDU = \int (T(t)-T_\text{base}) dt$. Commonly, the interval is a day, which is why it is also referred to as Growing Degree Days (GDD)~\cite{gdu_paper}. Seasonal (and geographical) variations in GDU accumulation have direct impact on the predicted harvest time. Thus, the harvest time is a probability density over time points, although further simplifications like mean harvest time and standard deviations or a simple time interval are conceivable as well. EAs work on discrete domains, so we will consider discrete time points and intervals. They do not necessarily have to be equidistant, but one usually considers days or weeks as units.

\subsection{Formalization}
Next, we introduce quantities that impact our problem. Let $\mathcal{D}=\{1,2,\dots,d_\text{max}\}\subset\mathbb{N}^+$ be the possible planting and harvesting dates. The dates themselves are encoded with an index $d\in\mathcal{D}$ as well as the earliest and latest planting dates for each species $s\in\mathcal{S}$, thus $d_\text{early}(s), d_\text{late}(s)\in\mathcal{D}$. We further introduce $\mathcal{S}$ as the set of species. Moreover, we have the required accumulated amount of GDU for each species to be harvested, i.e.~$g_\text{harvest}(s)$. Thus, we calculate the harvest date by $ d_\text{harvest}(s, d_\text{plant}) =$
\begin{align}\label{eq:havest_date}
    \underset{d}{\min{}} \left\{ d \geq d_\text{plant}\;\middle|\;\sum_{x=d_\text{plant}}^{d} g_\text{acc}(x) \geq g_\text{harvest}(s)\right\}\,,
\end{align}
with GDU accumulation function $g_\text{acc}(x)$.

Given all this information, we define a harvest matrix $\mathbf{H}\in\mathcal{D}^{|\mathcal{S}|\times |\mathcal{D}|}$ that contains the harvest date for each species (as rows) and each planting date (as columns). We assign the value $-1$ impossible planting dates. Thus, $\mathbf{H} = \{H(i,j)\}$ where $H(i,j) =$
\begin{align}\label{eq:harvest_matrix}
    \begin{cases}
    d_\text{harvest}(s=i, d_\text{plant}=j) & \text{if} \; j\in[d_\text{early}(i),d_\text{late}(i)]\\
    -1 & \text{otherwise}
    \end{cases}\,,
\end{align}
with harvest dates from Equation~\eqref{eq:havest_date}.

The precalculation of the harvest matrix reduces the schedule calculation time. With $\vec{q}=(q(s))\in(\mathbb{R}_0^+)^{|\mathcal{S}|}$ as the vector of desired harvest quantity for each crop species, we can write the date harvest $\vec{h}(\vec{d}_\text{plant})\in(\mathbb{R}_0^+)^{|\mathcal{D}|}$ as
\begin{align}\label{eq:harvest_schedule_calc}
\vec{h}(\vec{d}_\text{plant}) =  \sum_{s\in\mathcal{S}} \Big\{ \mathbf{I}_{d_\text{max}}(\mathbf{H}(s,d_\text{plant}(s)),d)q(s) \Big\}_{d\in\mathcal{D}}\,,
\end{align}
where $\mathbf{I}_{d_\text{max}}$ is the identity matrix with $d_\text{max}$ dimensions mapping dates to themselves again. For instance, a date-to-week mapping matrix yields the weekly harvest.

\subsection{Designing the Optimization Algorithm}
In this work, we consider the $(1+1)$-ES. This turns out to be a good choice in our experiments, and there is no optimal choice due to the \textit{No-free-Lunch-Theorem for Search} \cite{nofreelunch_theorem}. In the case that parent and child have the same loss value, we prefer the child to the parent, increasing the probability to overcome \textit{plateaus} in the loss landscape.

\subsubsection{Oscillating Mutation Rate.}
We draw the initial planting dates from a uniform distribution $\mathcal{U}$ by $d_\text{plant}(s)~\sim~\mathcal{U}[d_\text{early}(s), d_\text{late}(s)]$ for each species $s\in\mathcal{S}$ within its individual time window. Mutation is done by redrawing few elements. Further, we will use an initial mutation rate of $\frac{1}{|\mathcal{S}|}$, so we expect one planting to change by mutation. After this initial step, we exploit the advantages of an adaptive mutation rate~\cite{adaptive_mutation_1,adaptive_mutation_2} by introducing a counter $j=0$ that is increased by $1$ if the mutant is worse than the parent. This is done up to a maximum ratio $\rho_\text{max}$ of elements. If the mutant is better than the parent, the counter is reset to $0$. Moreover, there is the mentioned \enquote{plateau case} where the mutant is as good as the parent. In this case, the counter keeps its current value. Hence, we formulate a mutation rate that is oscillating between $\frac{1}{|\mathcal{S}|}$ and $\rho_\text{max}$ with frequency $\omega$.
\begin{align}\label{eq:mutation_rate}
\rho(j) = \frac{1}{|\mathcal{S}|}\bigl( 1+(\rho_\text{max}|\mathcal{S}|-1) \sin^2(\omega j) \bigr)\,,
\end{align}
which enables the algorithm to try a wide range of mutations and escape from local optima without turning into a random walk. This principle is connected with the concept of \enquote{temperature} in simulated annealing \cite{sa_paper} but with advantage to be adaptive to the landscape in vicinity of the current best solution.

\subsubsection{Hierarchical Loss Function.}\label{sec:loss}
First, we define the loss vector $\vec{L}(\vec{h}) = (L_+, L_-) = \left( \sum_{h\in\vec{h}_+} l_C(h), \sum_{h\in\vec{h}_-} l_C(h) \right)$ with
\begin{align}
l_C(h) &=
\begin{cases}
\frac{h}{C}\left(1-\frac{h}{C}\right)\, & \frac{h}{C} < 1\\
\exp{\left(\frac{h}{C}\right)}-\exp(1)\, & \frac{h}{C} \geq 1
\end{cases}\,,\label{eq:loss_function}
\end{align}
which has to be minimized in the following. $\vec{h}_+$ only contains harvest quantities above and equal to the capacity limit, and $\vec{h}_-$ only contains those below. We discriminate between two cases with different optimization goals. For $h(d) < C_\text{target}$, we want either full capacity harvest or no harvest at all, whereas for $h(d) \geq C_\text{target}$, we want to minimize overshoot. The total harvest is constant, meaning that decrease of harvest at one date simultaneously increases harvest at another date by the same quantity. Our loss promotes high-harvest dates getting \enquote{full} in terms of the target capacity, while simultaneously eliminating low-harvest dates. Thus, in case of $h < C_\text{target}$, two harvest quantities \textit{diverge}, provoked by a concave loss function. For $h \geq C_\text{target}$, the involved dates' quantities \textit{converge}, provoked by a \textit{convex} function.

Although this might sound like a multi-objective approach, we still formulate a single-objective problem by preferring the loss in the region above capacity limit ($L_+$) over the region below it ($L_-$). Thus, every net change from over into under capacity is promoted. This is what we call \textit{hierarchical loss} where hierarchy decreases with increasing element index in $\vec{L}$. The flexibility to include further objectives or knowledge at this stage is obvious since one may add loss categories.

\section{Application and Results}

In the considered dataset, two harvesting sites 0 and 1 are available. We will perform the optimization along two scenarios S1 and S2, targeting different objectives. In S1, the sites have weekly harvest capacity limits, whereas in S2, the capacity is not given. Here, a reasonable capacity should be found during optimization. The GDU accumulation for harvesting season 2020/21 is forecasted by using a historical dataset of \num{11} years for both planting sites. The accumulation curves are plotted in Figure~\ref{fig:gdu_forecast}.

The GDU accumulation forecast comes along with uncertainties. Fortunately, GPR models have the property of not just extrapolating unseen regions, but also giving the confidence of their predictions. The actual predicted value is a mean value with a Gaussian standard deviation. We can exploit this to propagate uncertainties and estimate the sensitivity of our optimization to different GDU accumulation scenarios, i.e. weather conditions, by resample many harvest matrices by the bootstrap technique~\cite{bootstrap} and evaluate our result for all. We further substitute the identity matrix in Equation~\eqref{eq:harvest_schedule_calc} for date-to-date mapping with a day-to-week mapping binary week matrix $\mathbf{W}\in\{0;1\}^{|\mathcal{D}|\times|\mathcal{W}|}$.

We further compared our method with two established EAs, namely NSGA-II and MOEA/D, by using two multi-objective optimization strategies each. For a short-time run of $10^6$ generations, all those variants are outperformed by our method. Additionally, we formulated the problem as a mixed-integer linear program (MILP) and solved it with CPLEX\textregistered{}. Here, we observed as well that our method leads to significantly better results than the exact one of the solver. For the sake of brevity, a more detailed elaboration and plots can be found in the Appendix.

\subsection{Harvest Schedule Optimization}\label{sec:optimization}
For the optimization scenarios, our proposed EA applies the adaptive mutation rate shown in Equation~\eqref{eq:mutation_rate}. We use $\rho_\text{max} = \SI{1}{\percent}$ and $\omega = \num{5e-4}$. The number of species $|\mathcal{S}|$ is \num{1375} for site 0 and \num{1194} for site 1.

Our hierarchical loss needs the capacity limit for the separation between the two regions. For S1, the target capacity is given. We split S2 into two slightly different approaches, S2-1 and S2-2, since \enquote{lowest capacity needed} is always a trade-off with the number of total harvest weeks. The optimization is exactly the same as for S1 but with different inference of the capacity limit. In S2-1, we link the target capacity limit to the number of harvest weeks the current solution needs. For S2-2, we determine the maximum number of possible harvest weeks. Thus, we expect S2-1 to need a higher capacity in less weeks whereas S2-2 needs a lower capacity in more weeks if not all possible weeks are harvest weeks after all.

In Figure~\ref{fig:harvest_results}, the weekly harvest quantity is plotted against the harvest weeks for site 1. The light yellow bars are the weekly harvest quantity if the plants are planted by the original planting schedule given in the data. Blue bars represent our optimized result after $10^9$ generations.

\section{Discussion}

In the following, we illuminate our optimization results from a technical as well as a social impact point of view.

\subsection{Optimization Process}
All scenarios show that the main optimization process needs orders of $10^6$generations to saturate at a moderately well optimized state. The optimization process plot in Figure~\ref{fig:loss_hist} shows that behavior exemplarily for two scenarios and three independent runs each. Beyond $10^7$ generations, fine-tuning occurs, e.g., if a constellation is found that a complete harvest week can be canceled for. The optimization process directly shows the characteristics and the strength of our method. Firstly, the hierarchical character of the loss is observable by a monotonically decreasing $L_+$ and an $L_-$ showing jumps to higher values. Secondly, fine-tuning preferably happens with higher mutation rates than the intermediate saturation. Using the adaptive mutation rate prevents the EA from getting stuck in local optima. More sophisticated optimization needs stronger mutations, i.e. higher mutation rates.

\begin{figure}
	\centering
	\includegraphics[width=0.85\columnwidth]{images/loss_hist_site1_sc2.pdf}
	\caption{Optimization process with 3 independent runs. The two lower plots show the loss vector components $L_+$ and $L_-$ against iterations. The upper plot shows the mutation rate for each successful improvement as a dot.}
	\label{fig:loss_hist}
\end{figure}

\subsection{Social Impact}

Our defined loss vector components have illustrative interpretations. For instance, $L_+$ minimizes food or crop waste by overshoot reduction, whereas $L_-$ minimizes logistic costs accompanied by greenhouse gas emissions. By construction, our hierarchy prefers food waste prevention to emission reduction. Firstly, it reduces the food waste by accepting additional emission. Secondly, the emission is reduced by distributing the yield preferably efficient. Given the hierarchy, no new food waste is created thereby.

Furthermore, the bootstrap shows that the optimization just for one GDU forecast \textit{overfits} on that single scenario. This is visible by the resulting error bars in Figure~\ref{fig:harvest_results}. Optimizing for many GDU forecast realizations is expensive due to many loss evaluations. Nevertheless, we observe that the uncertainty propagation from GDU forecast to harvest yield still stays within standard deviation. Thus, our optimization is robust against environmental forecast uncertainties and can schedule the harvest reliably. Additionally, the optimization can be adjusted on-the-fly, e.g. once a more precise forecast (harvest matrix) is available.

We introduce the overshoot (undershoot) reduction ratio $R_o$ ($R_u$) that describes, to which extent we can reduce food waste and emissions with respect to the unoptimized case given in the data. As shown in Figure~\ref{fig:harvest_results}, we can reduce \SIrange{62}{100}{\percent} of over- and undershoot for both sites and all scenarios which is a drastic improvement. Using our method, one could thus tackle global food waste on a supply chain as well as on a farming level, which additionally contributes to a more efficient agricultural land use.

\section{Supplementary Material}

\subsection{Detailed Description of the Dataset}
The given dataset was distributed by Syngenta within the scope of the Crop Challenge\footnote{\url{https://www.ideaconnection.com/syngenta-crop-challenge/}} 2021 and contains daily GDU time series from 01/01/2009 to 12/31/2019 for two sites (named as site 0 and site 1). Original, earliest, and latest planting dates of 2569 crop species (\num{1375} for site 0, \num{1194} for site 1) and the required GDUs for each of those are given. On this dataset, two scenarios are exercised with randomly drawn desired harvest quantities per species. They are sampled from Gaussian distributions $\mathcal{N}(\mu,\sigma)$ with $\mathcal{N}(250,100)$ and $\mathcal{N}(350,150)$, respectively. For scenarios S1, the sites have weekly harvest capacity limits (7000 units for site 0, 6000 units for site 1) whereas for S2, the capacity is not given. The optimization is performed for harvest season 2020/2021.

Plant and harvest windows for each plant species and both sites are shown in Figure~\ref{fig:plant_harvest_dist} colored in blue and orange, respectively, in the upper plots. For site 1, they are quite continuous but for site 0, one can see accumulations regarding possible planting days around February and May. As a result, one can see two accumulations for the harvest days around November and the following February. The plant and harvest day distribution for site 1 is considerably smoother, although accumulations can also be seen here. A deliberate choice of the individual yield per plant could equalize this effect. However, yields are roughly drawn from Gaussian distributions in our case. The lower plots in Figure~\ref{fig:plant_harvest_dist} confirm that by showing that weighting the day distribution by corresponding yield per species is not flattening the overall shape\textemdash neither for scenario S1 nor for S2.
\begin{figure}[t]
	\centering
	\includegraphics[width=0.9\columnwidth]{images/plant_harvest_dist.pdf}
	\caption{Distribution of possible plant and harvest days for all species and both sites. The upper plots show the spans (windows) of possible plant and harvest days for each plant species. The middle plots show the absolute abundance (frequency) of the respective days by summation over all species for the respective site. Additionally, the lower plots show the harvest day abundance weighted with the harvest yield for both scenarios of the corresponding species by multiplication.}
	\label{fig:plant_harvest_dist}
\end{figure}

\subsection{GDU Accumulation Forecast with GPR Model}
A naive approach to forecast the GDU accumulation would be to group the historical data by calendar date\textemdash i.e. month and day\textemdash and average over years. One would get an estimate for the daily jitter (or noise) by additional calculation of the standard deviation. Finally, the forecast could be a sampling of the future calendar days by a Gaussian distribution with the calculated means and standard deviations. However, this ansatz has some disadvantages. On the one hand, we have a problem when it comes to leap years. There is no clear assignment of \enquote{leap-year days} to \enquote{non-leap-year days}. However, this effect has not a severe impact, which is why we would just drop the leap days from the dataset. A more important drawback, on the other hand, is that this method is blind for possible underlying seasonalities or trends. This gets obvious if you think about trends due to climate change or seasonalities beyond annual scale, as\textemdash for instance\textemdash the El Niño phenomenon. Averaging over years would make those effects invisible or only noticeable by an increase of the standard deviations. This is, why we prefer to forecast the GDU accumulation by using a GPR model.

GPR models use kernels as priors for data prediction, \mbox{inter-,} and extrapolation. If we choose those kernels deliberately, we can include our knowledge about seasonality into the GPR process. We will choose a kernel that is a sum over the following fundamental kernels:
\begin{itemize}
	\item a periodic exponential kernel with annual periodicity to represent the obvious annual seasonality,
	\item a linear kernel to represent possible large-scale trends over the historic period (for instance climate change),
	\item a bias kernel to cope with the fact that the GDU values are not centered around zero, and
	\item a white noise kernel to estimate random daily temperature fluctuations.
\end{itemize}

The forecast results for the GDU accumulation functions are shown in Figure~\ref{fig:gdu_pred} for both sites. Although there are some anomalies at some days, one cannot see any other periodic behavior in the residue plots in the two figures that would not have been caught by the GPR model. Thus, we can state that the GPR model covers the given dataset quite well. Due to the periodicity, we can simply extrapolate into the future (i.e. $d\geq 0$). The result for both sites is shown in Figure~\ref{fig:gdu_forecast}. All in all, the GDU data of site 1 has a higher volatility than site 0 which is reflected in the overall higher standard deviation compared to site 0.

\begin{figure}[ht]
	\centering
	\includegraphics[width=0.9\columnwidth]{images/GDU_GP_pred_site0.pdf}
	\includegraphics[width=0.9\columnwidth]{images/GDU_GP_pred_site1.pdf}
	\caption{GPR prediction result for site 0 and 1. In order to get a more compact optimization domain, the day indices are normalized to years. In the upper plot, the data is shown as well as the mean prediction result and the confidence band with represents $\pm 2 \sigma$ in terms of Gaussian standard deviation. The lower plot shows the residues with respect to mean with the standard deviation of prediction as error bars.}
	\label{fig:gdu_pred}
\end{figure}

\subsection{Harvest Schedule Results for Site 0}
Figures~\ref{fig:loss_hist_s1_site0} shows the optimization process for S1 and site 0. The optimization results for all 3 scenarios are shown in Figure~\ref{fig:harvest_results_site0}.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.9\columnwidth]{images/loss_hist_site0_sc1.pdf}
	\caption{Optimization process for scenario S1 and site 0. The different colors represent 3 independent runs. The two lower plots show the components of the loss vector $L_+$ and $L_-$ against number of iterations. The upper plot shows the mutation rate for each successful improvement as a dot.}
	\label{fig:loss_hist_s1_site0}
\end{figure}
\begin{figure}[ht]
	\centering
	    \includegraphics[width=0.9\columnwidth]{images/harvest_site0_sc1_run02.pdf}
	    \includegraphics[width=0.9\columnwidth]{images/harvest_site0_sc2_run01.pdf}
    	\includegraphics[width=0.9\columnwidth]{images/harvest_site0_sc3_run02.pdf}
	\caption{Weekly harvest quantity for all 3 scenarios on site 0. The orange bars symbolize the harvest per week for an unoptimized schedule given in the dataset. Blue bars show our optimization result. The dashed lines mark $C_\text{target}$ (red) and $C_\text{need}$ (purple). For the uncertainty estimation, plotted as error bars, \num{100} harvest matrices are drawn from the GPR model for the GDU accumulation functions.}
	\label{fig:harvest_results_site0}
\end{figure}

\subsection{Cyclic harvest weeks}
In addition to a single harvest time window, it is possible to define a day-to-week mapping for a cyclic harvest season $\mathbf{W}_\text{cyclic}~\in~\{0;1\}^{|\mathcal{D}|\times 52}$ by
\begin{align}
    \begin{aligned}
       \mathbf{W}_\text{cyclic} &= \{W_\text{cyclic}(i,j)\},\\
       &W_\text{cyclic}(i,j) =
        \begin{cases}
        1 & \lfloor \frac{i+\delta}{7}\rfloor\bmod{52} = j\\
        0 & \text{otherwise}
        \end{cases}\,,
    \end{aligned}
\end{align}
where the 52 arises from the number of weeks per year. This means that one considers the same seeding over multiple seasons, i.e. repeating harvest plans can also be optimized. Figure~\ref{fig:harvest_cyclic} shows an exemplary optimization result of site 1 and scenario S2-1. Especially in this case, the overshoot is reduced to below \SI{0.2}{\percent} with respect to the capacity limit. The optimization process is shown in Figure~\ref{fig:loss_hist_cyclic}.
\begin{figure}[t]
	\centering
	\includegraphics[width=0.9\columnwidth]{images/harvest_site1_sc2_cyclic_run02.pdf}
	\caption{Weekly harvest quantity for scenario S2-1 and site 1 with cyclic harvest weeks. In the legend, all benchmark values are stated (cf. Table~\ref{tab:benchmarks}). For the uncertainty estimation, plotted as error bars, \num{100} harvest matrices are drawn from the GPR model.}
	\label{fig:harvest_cyclic}
\end{figure}
\begin{figure}[t]
	\centering
	\includegraphics[width=0.9\columnwidth]{images/loss_hist_site1_sc2_cyclic.pdf}
	\caption{Optimization process for scenario S1-1 and site 1 with cyclic harvest weeks. The different colors represent 3 independent runs. The two lower plots show the components of the loss vector $L_+$ and $L_-$ against number of iterations. The upper plot shows the mutation rate for each successful improvement as a dot.}
	\label{fig:loss_hist_cyclic}
\end{figure}

redpajama_set_name: RedPajamaArXiv


9 dead in shooting at historic Charleston African American church.
Police are searching for a gunman who opened fire Wednesday night at a historic African American church in downtown Charleston, S.C. Charleston officials said 9 people were killed and others injured.
"I do believe this is a hate crime," the police chief, Greg Mullen, said at a late night briefing, without explaining the basis for his conclusion. He said as far as he knew there was only a single gunman.
Authorities said the victims were involved in a prayer meeting at the time of the shooting.
They are hunting a white male, described by police as clean-shaven, in his early 20s with a small build, wearing a gray sweatshirt, blue jeans and Timberland boots.
The "message is there was one hateful person who did this dastardly deed," the mayor said at a late night meeting.
Police said there were survivors but did not say how many or what condition they were in.
The Charleston Police Department has not yet identified the victims. Police said the shooting occurred about 9 p.m. at the Mother Emanuel African Methodist Episcopal Church between Henrietta and Calhoun streets near Marion Square in downtown Charleston.


Why do we recommend this country at this time as a country to seriously consider for acquiring a banking license and incorporating your bank?
1. This country has a very stable political and economic situation. This country is headed for over many years where both the executive and the legislative are governed by the same majority.
2. You should also choose this country to be your offshore banking jurisdiction because it is not a micro-country which can be suspected to be an offshore center for your clients and for their local tax department. As you know an aggressive campaign against the offshore financial industry has begun leaded by the U.S and the European Union.
3. Although the $37.5M capitalization requirement for an international (offshore) license is higher then in some others jurisdictions, only $1,000,000 of that actually has to be held by the government bank – the rest can stay with the bank as working capital. Most jurisdictions require a lot more, if not all of the minimum capitalization amount to be deposited – and frankly we don't know of any jurisdiction where much less then $37.5M will now obtain you a bank license even if the official minimum capitalization amount is still only $1M.
4. Fast track (60 day) sponsored SWIFT membership. The banking superintendent department takes care of the application process for this most important bank facility and allows your bank to be a member without waiting the normal two year waiting period that will usually apply for new banks.
5. Guaranteed sponsored correspondent account opening. Banks formed in most jurisdictions today have a very difficult time in obtaining bank correspondent accounts with major banks when they are just starting out.
6. Strongest banking secrecy laws in the world. Since this new nation is a neutral country, the U.S. and Europe have largely left this country alone and have not brought any pressure to bear for banking privacy laws to be amended. Finally, its banking system is safe, reliable and internationally recognized.
7. Decision guaranteed within 90 days – please note that the decision may be a request for further information or documentation, but that is never a bad sign.
8. This country has a modern business and technology infrastructure inside a Regional Community.
9. This country provides you the opportunity to grow your bank with relatively little bureaucratic red tape.
10. This country's economy is fully free and open to the rest of the world. However, its banking system is hardly influenced by the World banking situation. Legislation and banking supervision are very lax and allows almost everything against fee.
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The average cost to form a bank here is about $150,000 in licencing and legal fees. It could be somewhat less or it could be a little more, depending on what professional services you provide yourself or whether you need turn key assistance with things like IT integration, or preparing the business plan and provision of a local senior bank director to serve as the bank's local officer.


The house is actually used for storing drugs. This is not being used for living or sleeping. We found a small store, but it is not permitted inside the subdivision…. You'd think that things were being sold, but they're actually shabu, Aquino said, referring to another name for crystal methamphetamine.


text:
Horizontal Themes
"The practical success of idea, regardless of its inherent advantages, depends on attitude of people."
Nikola Tesla.
Given their relevance for Agenda 2030 and the context of Bosnia and Herzegovina, two horizontal themes have been determined which are interwoven through three mentioned development pathways, such as: "Investment in human capital for future", and the "Leave no one behind" principle.
Human Capital for future
Human capital means intangible collective resources possessed by individuals and groups within certain population. Human capital includes not only knowledge and skills but also experience, creativity, individuality, good health and moral character. In the business-organizational sense it is deemed that joint investments of an employer and employees in human capital contributes on a long-term basis not only to enterprise development but also to the whole society. Looking at it through a prism of public policies, citizens and public policies have to jointly invest in human capital and that human capital has to be valued.
Human capital is one of the basic factors of development and its fostering is among key priorities in countries. Many research in Bosnia and Herzegovina has indicated that insufficient investment in human capital, but also valuation of the existing one, result in population outflow, which is substantially increasing every year. Just because of importance of human capital, the World Bank calculates the Human Capital Index, and according to the latest data as of 2017, Bosna and Herzegovina is ranked on the 58th position of 157 countries, with the Human Capital Index of 0,62. It means that a child born in Bosnia and Herzegovina today will achieve 62% of his/her full productivity as an adult in relation to what he/she could be with completed full education and with full health care.
Considering the fact that accelerated development is not possible without strong human capital, this Framework strives through the existing development pathways (Smart Growth, Society of Equal Opportunities, and Good Governance and Public Sector Management) to incorporate the importance of investing in human capital and its valuation. Recognizing human capital as a crucial factor for development and implementation of the policies which contribute to human capital development may prevent the population outflow, but also result in potential return of the population that has already left.
Principle "Leave no one behind"
Introducing the "Leave no one behind" principle in practice requires mechanisms of management and accountability from the local to the international level. It also means that there are certain measures enabling that a progress in implementing the "Leave no one behind" principle is visible through steps being taken towards achieving the targets related to accomplishing equality. It will not be possible to achieve the SDGs if the poorest and most vulnerable remain neglected and excluded from development processes or if they do not have benefits from the achieved progress and enhancements in the society. It is important to explore which persons are neglected and left behind in Bosnia and Herzegovina in a broader sense, in which parts of the country they live, what the causes and consequences of their negligence are and which policies have to be defined in order to improve their position in the society.
In Bosnia and Herzegovina the concept "Leave no one behind" has been earlier incorporated through the prism of social inclusion and social protection. However, this concept goes deeper than the previous approach and it calls for combination of targeted policies in order to enhance the condition of the most vulnerable categories of population and policies targeted on more equitable and sustainable growth and on creating an environment which actively encourages inclusion of everyone in the decision-making processes, with full observance, protection and exercising human rights. Due to that reason, the accelerators and drivers under three development pathways emphasize a need to focus on policies and measures targeted on the neglected and excluded part of the population (affirmative measures).
The SDGs Framework in Bosnia and Herzegovina
The Process of Designing the Sustainable Development Goals Framework
Key principles of the SDGs Framework in Bosnia and Herzegovina
Vision and development pathways by 2030
Financing the SDGs implementation
Monitoring and reporting on progress in accomplishing the Sustainable Development Goals in Bosnia and Herzegovina

redpajama_set_name: RedPajamaCommonCrawl


This beautiful holiday home, situated in a lovely quiet residential area, is only ten minutes from the Disney parks and a few minutes drive from two huge Walmart supermarkets. There are many restaurants, bars and shops located within easy reach. This vacation rental Villa: the Florida room is adjacent to the spacious kitchen, has two large comfy sofas, cable TV and DVD, a music system, free High Speed Internet and is one of two lounge areas. There is a formal dining area, which seats six people, and the second lounge which has comfortable seating for five.
All four bedrooms are upstairs and the master suite, with King size bed and cable TV, has a large en-suite which includes a bath, separate shower cubicle and his 'n hers wash basins. There is also a large walk-in wardrobe. Bedroom 2 is also en-suite and has a Queen size bed and cable TV. Bedroom 3 has twin beds and cable TV and bedroom 4 has bunk beds for children and a TV/DVD. The large private pool (heated) has plenty of sunbathing/dining space and sunbeds and chairs.
Cancellation policy: If a booking is canceled… more than 4 weeks prior to arrival a $35.00 administration fee is charged between 4 and 2 weeks prior to arrival $100.00 is retained. Between 2 and 1 weeks prior to arrival $200.00 is retained. Less than 1 week prior to arrival 50% of the rental is retained.
Additional Services Available: Cribs - Highchairs - Airport Meet & Greet Service. First Night Welcome Packs. Discounted Attraction Tickets are available – details on request. Gourmet Catering Service – for those nights in without the cooking! Nanny Service – for help with the kids throughout the day. Attractive Car Hire Rates.


text:
Coffee House General Election 2017
U-turns matter less than journalists think, especially if voters like the result
Five reasons why the 'dementia tax' U-turn was inevitable
Will Heaven
This is the latest in a series of posts that could be entitled 'things I could never say when I was a political journalist but now I can.' Today's topic: U-turns. The bottom line: they don't matter, or at least, they don't matter anywhere near as much as most of the coverage of them suggests.
Theresa May has changed her mind on social care. Political Twitter is still, as I type, in the sort of excited spasm that suggests that this is an event that will change EVERYTHING.
To some, Mrs May has shattered forever her image as a strong, steady leader. A slightly more sophisticated take suggests that she's shattered her relationship with the Conservative Party, which will never trust her again.
I'm not convinced by either of those arguments, to be honest. I think the May brand among voters can survive this. As for relations in the Conservative Party, this will simply persuade those colleagues who believe Mrs May governs through a small, opaque clique that they were right. I doubt it will change the way she governs, not least since her approach thus far has made her PM and put her on course for a healthy general election win; weighed against those things, today's difficulties are still small beer, and certainly no reason to panic.
Media coverage of 'U-turns' almost always proceeds on the implicit, possibly unconscious assumption that they are a Bad Thing: voters, it is supposed, will see the author as weak and indecisive.
Yet there's not much evidence to support that supposition, which usually misreads causation. Simply, leaders who are already seen as weak and indecisive who then carry out U-turns will have those U-turns taken as a sign of their weakness and indecisiveness. The U-turn, if you like, confirms voters' views, but does not change it.
Likewise, if you're already seen as strong and decisive, then U-turning may well be seen as confirmation that you're strong and decisive, able to admit when you've made a mistake and put it right. After all, the alternative is pigheadness.
My suspicion is that Mrs May will emerge from today's excitements largely unscathed, though she should probably aim for some slightly more assured clips for the evening bulletins, repeatedly and calmly saying: 'Some people had some worries about our proposals, so we're going to listen to them and get this absolutely right before we do it.'
The key message is honesty: there's nothing wrong with saying you're changing your mind because voters were unhappy. Voters know that politicians want their votes: there's no penalty for a politician admitting to being a politician.
The cost arises when you're seen as dishonest about this: Gordon Brown didn't suffer because he changed his mind about a snap election in 2007. He suffered because voters thought he was lying about it. That shattered his carefully-crafted image for being straight and honest.
Mrs May, in calling this election, clearly learned from Mr Brown's strategic error in passing up that 2007 chance to obliterate his opponents. Now, she needs to learn from another of his mistakes.
As for the policy at the heart of this drama, I'd argue (and I recognise this view might not be shared by many Spectator readers, and especially not by the fearsome Will Heaven) that at the heart of it there remains a sensible and brave idea: that people with large, valuable assets should make some contribution to the costs of their care.
Or rather, that the children of those asset-holders should make a contribution, because that's what it means to recoup some care costs from estates after death. It doesn't mean taking money from the frail and elderly, since payment only comes when those people have sadly passed away. It means reducing the money that flows to their children.
Outrageous? Perhaps, but I'm afraid I don't share our national worship of inherited wealth. Broadly, I'd rather see less tax levied on incomes (which largely reflect effort) and more on wealth (which largely doesn't).
After all, what's the alternative to taking care costs from the estates of those who use that care? OK, the best alternative would be to take it from all estates dependent on their size, in the form of a larger inheritance tax.
But assuming that's not currently politically viable (I can still dream) then the only choice left is to fund care from general taxation, which will, to some extent, mean that the working poor end up paying tax to fund the care of wealthy pensioners living in large houses, and thus paying tax so that the children of those wealthy pensioners don't have to pay tax when they inherit those houses.
Yes, Mrs May's manoeuvring today will grab the headlines and excite Twitter, but there's something rather more important here. As long as she sticks with a policy that raises money for care from housing assets that would otherwise be inherited tax-free, she's heading in the right direction.
dementia tax
This election will be remembered as a triumph for the wealthy
Nigel Farage will always have more power outside Parliament
Why do voters find it hard to trust politicians? Because of all the broken promises
Forget Michael Gove or the rise of the Remainers. The reshuffle is about the march of the moderates
Theresa May will campaign as being tough on Brexit, but soft on society
Why the Midlands will matter on June 8th
William Cash


Some years ago I had the opportunity to spend a whole month holiday in Portugal. I didn't want to ask my friends, or neighbours, to water my plants everyday for me. I thought therefore to build a system for that purpose using the most common microcontroller PIC16F84A. I thought and tested lots of different solutions in order to find the best one. Reliability was the most important aspect to ensure: a fault could have killed my plants or, worse, flooded my neighbour's house.
Some years ago, in order to make some experiment at home with the PIC16F84A microcontroller, I needed to build a programmer. Surfing on internet I found lots of solutions and I decided to realize one of the easiest ones: a JDM programmer.The cost of the project is probably a couple of Euros. In the PCB in figure 1, I used only components I had available at home and for that reason the DIP socket is 20 pin instead of 18.
What I'm getting ready to explain below, is an original solution I adopted to backlight the thermometer's project SP0003. Thanks to this, I solved quite easily the main difficulty that consists in generating an even illuminated background to the LCD display using some common LEDs.
A bromograph (or UV Exposure Unit, or UV Screen Exposure System) is a fundamental instrument for making PCBs using the photo-etching method. Unfortunately its price is usually prohibitive for a normal DIY maker and the UV-neons are expensive anyway if you're thinking of building one.
I want to clarify from the outset that this is an ambitious project. Making a watering system that interacts with the environment and autonomously decides if it will irrigate some plants and how long, is not a trivial matter. For this reason, even if the version 1.00 works quite well and it is already operative in my balcony, probably many others will follow soon.


text:
Modern Engagements - Should I Propose to Him?
Article by MiaDonna
It's a tale as old as time. A boy gets down on one knee with the perfect diamond engagement ring and asks the love of his life to marry him. It's in all the movies and it's what everyone expects. But wait, why does it have to be that way? Who said we had to wait for the man to take charge of the engagement?
The fact is times are changing. Many traditions of old are simply being replaced with more modern ideas. Lab grown diamonds are replacing earth mined diamonds. Brides are opting for colored dresses instead of white. More couples are saving money by eloping instead of throwing lavish weddings. In addition, we're also seeing a shift in who is doing the actual proposing.
In an April 2020 survey about engagements, more than 10,000 people were interviewed and the results support this new change in protocol. The survey found that 57% of the women said they would propose to their boyfriend. What might be even more surprising is that 76% of the men thought women SHOULD propose. There's clearly a new trend starting now, and we're absolutely here for it.
Which ladies have taken control of the engagement and proposed to their boyfriends?
When you first think about proposing to your boyfriend, it may not feel "right" because you don't know anyone else who has done it. But the thing is you do! There are incredible female celebrities that you know and love who have indeed proposed.
Did you know that Kristen Bell proposed to her husband Dax Shepard? They didn't want to get married until the state of California legalized marriage for every couple. Once same-sex marriage was legalized, Kristin was the one who asked Dax to marry her.
Singer Pink also proposed to Carey Hart with a pit board stating, "Will You Marry Me?" during the Pro 250 class finals. They later married in Costa Rica in 2006.
Even Elizabeth Taylor proposed to one of her eight husbands! Michael Wilding received a proposal from the actress, businesswoman, and humanitarian.
Why should you consider proposing?
If you want to propose to someone, don't let any gender stereotype hold you back. After you and your partner have discussed engagement, either one of you should be able to do it. Remember, a proposal can be a surprise, but an engagement shouldn't be. Make sure you and your boyfriend are on the same page about getting married before either of you makes it official. After that, go for it! You should feel empowered to tell him that you want your "forever" to include him. Don't wait for him to tell you.
How should I propose?
Now that you've decided to pop the question, how will you do it? Planning a proposal can be such an exciting time and you know your relationship best. Whether you do it at home, on vacation, at a restaurant, or surrounded by friends and family, those details will be based on your relationship. That said, an engagement ring has historically been the centerpiece of a proposal. If you want to maintain that tradition, you can propose with a wedding band for him, or you can get a ring that you will wear following the engagement. If you get a ring for yourself, we suggest choosing a simple solitaire with a "place-holder" center stone, like MiaDonna's Diamond Hybrid®, and then choosing your "official" engagement ring together. Did you know we have an upgrade program where you can get a full credit to upgrade to a lab-grown diamond and the setting of your dreams. But if you know what you want and don't mind paying for it yourself, then go ahead and get the ring of your dreams for the proposal. After all, it's about doing what feels right for you and your partner.
Now that you know you're not alone in wanting to propose, we say Go For It! Life is short and you should celebrate the people you love in whatever way feels right for you and your relationship.
Bride & Groom Guide
Top 7 New Year's Eve Proposal Ideas
The New Year is fast approaching. You want to propose to your loved one, but want it to be a memorable experience and hear that much anticipated "yes!" Get inspiration with these unique New Year proposal ideas.
Wedding Donkeys and Other Unique Wedding Ideas
Plan a wedding that will be unforgettable. Here are some unique ideas for that special day, including incorporating a donkey into the celebration.


Senate Education Chair Patricia Rucker, R-Jefferson. Jan. 15, 2019.
SB 1 is this year's version of what was known in 2018 as the "free" community and technical college bill. Last year's bill had bipartisan support, and passed in the Senate, but later died in the House Education Committee.
"This bill [is] very near and dear to my heart," said Deb Harris, a Temporary Assistance for Needy Families, or TANF, coordinator for BridgeValley Community and Technical College.
Deb Harris, a TANF coordinator for BridgeValley CTC, speaking before senators on behalf of SB 1. Jan. 15, 2019.
Speaking before the committee, Harris, a single mom and BridgeValley graduate, said she sees students struggling to put food on the table.
The bill would create an $8 million grant program to cover tuition and fees at community and technical colleges for students to tap into after all other forms of financial aid has been exhausted.
To be eligible, a student would need to fill out the Free Application for Federal Student Aid (FAFSA), be at least 18-years-old with a high school diploma or equivalent, pass a drug screening, and agree to remain in the state as a taxpayer for at least two years after graduation and fulfill some community service.
On Tuesday, Senate Education considered a near-replica of last year's bill, but with a couple changes.
The new bill provides opportunities for younger students to participate in advanced technical programs. Under SB 1, ACE programs (Advanced Career Education) would be amended to allow middle school-aged students to participate. The program currently provides high school graduates with the opportunity to enter an advanced technical program of study for targeted industries.
A section of the bill was clarified to ensure it would include and represent all community and technical colleges in West Virginia. No other amendments were considered in committee.
The bill now goes to the Senate Finance Committee for consideration and will require a vote by both the full Senate and House.
Could the 'Free' Community and Technical College Bill Reappear in 2019?
A bill West Virginia Public Broadcasting followed closely during the 2018 regular state Legislative session could resurface in 2019 – legislation that would offer tuition assistance to in-state students attending a Community and Technical College. Last year, it was often referred to as the "free community and technical college bill," and it would've provided the "last dollar in" after all other forms of financial aid had been exhausted.


text:
Paul Imbs (* 4. Mai 1908 in Schlettstadt; † 27. April 1987 in Nancy) war ein französischer Romanist, Sprachwissenschaftler und Lexikograf.

Leben und Werk
Imbs studierte in Straßburg bei Ernest Hoepffner und Mario Roques und lehrte als Agrégé de grammaire nacheinander in Lille (Lycée Faidherbe 1931 bis 1933), Straßburg (Lycée Kléber 1933 bis 1938), Saint-Maur (Lycée Marcelin Berthelot 1938 bis 1944, 1943 auch an der École normale supérieure de jeunes filles in Fontenay-aux-Roses) und an der Universität Caen (Assistent 1944). Ab 1946 lehrte er an der Universität Straßburg (Maître-Assitant 1946, Maître de Conférences 1948, Nachfolge von Ernest Hoepffner auf der Chaire de philologie romane 1953). 1956 vollendete er seine Habilitationsschrift über die Propositions temporelles en ancien français und gründete das Centre de philologie et de littérature romanes. 1957 veranstaltete er in Straßburg ein Kolloquium über ein zu schaffendes großes wissenschaftliches Wörterbuch des Französischen, für das er 1960 als Recteur d'Académie in Nancy mit gaullistischer Unterstützung das Centre de Recherche pour un trésor de la langue française  gründete, das schon 1961 mit dem größten damaligen Computer ausgestattet wurde und von 1971 bis 1994 mit mehr als 100 Mitarbeitern unter dem Titel Trésor de la langue française 16 Bände von A bis Z publizierte, die seitdem digitalisiert wurden und im Internet frei konsultiert werden können (Analyse et traitement informatique de la langue française). Imbs schrieb für den ersten Band ein lexikographisch bedeutendes Vorwort.

Seit 1972 war Imbs Mitglied der Académie des Inscriptions et Belles-Lettres. 1973 widmeten ihm Schüler und Freunde die von Robert Martin und Georges Straka herausgegebene Festschrift Mélanges de linguistique française et de philologie et littérature médiévales offerts à Paul Imbs, Paris (mit Schriftenverzeichnis). Imbs war Ehrendoktor der Universitäten Louvain und Gent, Ehrensenator der Universität Karlsruhe und ab 1966 korrespondierendes Mitglied der Accademia della Crusca. Er war seit 1968 Ehrenbürger von Sélestat und seit 1986 Offizier der Ehrenlegion.

Weitere Schriften
 L'Emploi des temps verbaux en français moderne. Paris 1960.
 (Hrsg.):  Lexicologie et lexicographie françaises et romanes: orientations et exigences actuelles. Centre national de la recherche scientifique. Colloque international, Strasbourg, 12–16 novembre, 1957. C.N.R.S., Paris 1961.
 Le subjonctif en français moderne. Strasbourg 1953.

Literatur
 Imbs, Paul. In: Dictionnaire de biographie française. Band 18, 1990, S. 158–159.
 Paul Metz: Imbs, Paul. In: Nouveau dictionnaire de biographie alsacienne. Faszikel 18, 1991, S. 1737.
 Nachruf durch Robert Martin: Paul Imbs. In: Revue de linguistique romane, 51, 1987, S. 671–673.

Weblinks

Anmerkungen

Romanist
Sprachwissenschaftler
Lexikograf
Hochschullehrer (Universität Straßburg)
Ehrenbürger in Grand Est
Ehrenbürger im Elsass
Ehrensenator des Karlsruher Instituts für Technologie
Mitglied der Ehrenlegion (Offizier)
Mitglied der Académie des Inscriptions et Belles-Lettres
Mitglied der Accademia della Crusca
Ehrendoktor der Universität Gent
Person (Sélestat)
Franzose
Geboren 1908
Gestorben 1987
Mann

redpajama_set_name: RedPajamaWikipedia


text:
Q: Trying to link a variable within a label to a button I'm a newer developer and I'm trying to figure out how to navigate to a specific CollectionView based on a randomly generated variable within a label.
I created a button that generates a random weather pattern and within there is a weatherType variable that's randomly generated and when the labels weatherType is "Sunny", "Windy", "Rainy", or "Cloud" I want to be able to press another button that will take the user to a SunnyCollectionView, 'RainCollectionView','WindCollectionView', or 'CloudCollectionView'.
class ViewController: UIViewController {

    @IBOutlet weak var firstLabel: UILabel!
    @IBOutlet weak var weatherInfoLabel: UILabel!
    override func viewDidLoad() {
        super.viewDidLoad()
        // Do any additional setup after loading the view.
    }

    @IBAction func weatherButton(_ sender: Any) {
        let weatherVar = Weather()
        firstLabel.text = weatherVar.weatherInfo()
    }

    @IBAction func clothingCollectionButton() {
        let weatherVar = Weather()
        let storyBoard : UIStoryboard = UIStoryboard(name: "Main", bundle:nil)

        if weatherVar.randomWeatherType == "Sunny" {
            let nextViewController = storyBoard.instantiateViewController(withIdentifier: "SunCollectionView") as! SunCollectionViewController
            self.present(nextViewController, animated:true, completion:nil)
        }
    }

From here, I'm able to use the weatherButton to get a random weather type but I want to use the clothingCollectionButton to take me to the next collection view based on that weather.
When I'm simulating and I hit the clothingCollectionButton, it does not take me to the CollectionView based on the random weatherType within the label.
Could anyone help? What I have up is what I thought would work but it hasn't.

redpajama_set_name: RedPajamaStackExchange


The hardest thing about learning anything OO related is simply getting over the language barrier. Anytime you talk to an OO afficionado, you always end up hearing a term you don't understand. My biggest problem with learning Model-Glue, Reactor and ColdSpring, three things I really want to start using, is finding out what IOC, AOP, DAO, Beans, Transfer Objects, Gateway Objects, and all of the other terms mean, so that I can actually make it all the way through the documentation. Google searches are alright, but I've never had one spot I could go to find out what it all meant.
To fix that, I put a copy of CanvasWiki, by Ray Camden up at ooGlossary.com. If you have a good definition for a term, please feel free to add it. These will hopefully be edited by the community at large, and we should end up with a good resource for all of that OO language goodness.


The Ballyliffin TownHouse is situated in the centre of Ballyliffin. Ideal for exploring The Wild Atlantic Way, Playing a round at The Ballyliffin Golf Club, attending a wedding in one of the nearby wedding venues in Ballyliffin,(All less than a 2 minute walk), A visit to Malin Head or having that time out you always deserved.
From Derry to Buncrana, Clonmany, Ballyliffin.
Maps are provided to help you on your Journey.


text:
London Transit briefs
The buses still aren't on time — but they're getting closer.
Megan Stacey
The buses still aren't on time — but they're getting closer. A new supervisor position was created at the London Transit Commission (LTC) earlier this year to better track performance. The first review tackled "on-time reliability" by sending the supervisor out onto the bus to ride routes that often miss the mark when it comes to scheduled stops. Buses were on time 76 per cent of the time in June 2018, below the 80 per cent target. (And buses are considered on time as long as they arrive at a stop within five minutes after the scheduled time.) The on-time rate is down slightly over March and April, 77 per cent each, because of frequent summer construction projects that can slow down transit routes.
Ridership on London public transit is up 2.7 per cent over last year in the first half of 2018. More than 13-million passengers hopped on the bus between January and July 2018, compared to 12.77 million in the same period in 2017. But the good news doesn't necessarily translate to increased revenue. The average fare is lower, down to $1.38 compared to $1.40 in the first half of 2017. LTC staff attribute the drop in part to the introduction of more subsidized rates, such as the discounted monthly bus pass for low-income Londoners.
Four bus shelters the LTC planned to install have fallen off the list because the structures would intrude onto nearby properties. The top 10 stops in need of more protection got the OK from the transit commissioners in April, but those installations can be spiked if property deals can't be inked. Shelters pegged for Adelaide at Little Simcoe, Oxford at Wharncliffe, Wellington at Grand, and Oxford at Gammage were nixed and nine new locations were approved by the transit commission Wednesday night.
 Ex-workers can be part of harassment probe: London Transit boss Florist's giveaway encourages 'good neighbours'
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VANCOUVER, – CITT has announced a new partnership with the Acsenda School of Management. The partnership grants students who have completed Acsenda's BBA program advanced standing towards the academic requirement of the CITT-Certified Logistics Professional (CCLP®) designation.
"We're very excited to begin this relationship with Ascenda, and offer their learners an accelerated path to becoming CCLP designation holders" said Pina Melchionna, CITT's President & CEO. "After examining their program and its learning outcomes, we're confident that graduates of Acsenda's BBA program will possess the business knowledge acumen we expect from our certified designation holders. We were very impressed and pleased to make Acsenda one of our first new partners as CITT looks to make our CCLP designation even more accessible to those looking to advance in the supply chain, transportation, and logistics field".
CITT learning and professional development offerings are all affordable, accessible online and have the best ROI in the business. Visit the CITT website at www.citt.ca for more information.


The past year of 2014 was a stinker. I'm ready for it to be over and done.
For the coming year, I'm more optimistic. The nation — and world — can't stay in the blahs forever.
So you got to hang on 'til tomorrow, come what may!
Maybe the sun will come out again in 2015 for most of us.
• I hope the world, especially Germany and Europe, will recognize the immense danger they face from Russian leader Vladimir Putin. While we're currently focused on the Middle East, it's Putin's ambition for regional dominance that is the bigger threat to world stability. Should Putin move to annex all of the Ukraine or Baltic states, it would be a direct challenge to NATO and the West. I'm not sure the U.S. or Europe has the guts to stand firm against Putin, just as the West caved in the 1930s to Hitler's ambitions. We hope that the collapse of the world oil market and sanctions will be enough to push Putin back, but that instability could also make him even more dangerous.
• I hope that U.S. leaders now recognize that we are idiots when it comes to our policies and adventures in the Middle East. We don't understand that part of the world, how its people think or how its leaders manipulate us. We stupidly removed the one secular strongman in the region when we invaded Iraq. Saddam was a tyrant, but he was a counterbalance to Iran and the Islamist movements. The U.S. has been extremely naïve in its dealing with that region over the past 20 years. It's time we grow up.
• I hope that the U.S. will stand firm with Israel in its ongoing war with terrorists from Hamas and other terror movements in the region which seek to destroy that nation. Europe is turning its back on Israel; anti-Semitism is on the rise there and some European political leaders have openly taken the side of the Palestinians against Israel. The U.S. under President Obama has been lukewarm to Israel, too. It's in our interest and in the world's interest that Israel finds stability. Israel will always have enemies. That goes back over 2,000 years. The U.S. needs to step up and do the right thing and support its only friend in the region.
• I hope the U.S. economy will finally make a swing up in 2015. Some parts of the nation are already seeing growth and have more or less recovered from the recession. But there are many other areas, including Northeast Georgia, that continue to suffer from unemployment and a weak housing market. Especially in Barrow and Jackson counties, the dead housing market has been a killer. And the reality is, neither county will see that rebound until the housing market in Gwinnett County rebounds. Much of the residential growth in Barrow and Jackson comes from people moving out of Gwinnett. Let's hope 2015 will be the year real estate turns around here.
• I hope the new Republicans in Congress won't act like idiots. There are a few Republican knuckleheads in that bunch, including some from Georgia. Many have misread what citizens want. The public doesn't want Republicans to sling swords or slash and burn. Citizens want Congress to function. They're tired of all the political gamesmanship from both political parties. Those on the far right who are calling on the new Republican Congress to wage war on Democrats are misguided.
• I hope that some quality candidates for president emerge in 2015. So far, I'm not impressed with the names being tossed around in either party.
• I hope Congress will find a way to fix our broken immigration system. That issue has become a political hot tamale and Republicans have long avoided having to vote on any kind of plan. It's time to man-up and fix what we all know is a dysfunctional, inadequate system of dealing with both legal and illegal immigrants. Illegal immigration would no longer be an issue if Congress would fix this problem.
• I hope our local tax digests see some growth in 2015. The collapse of the real estate market here and the resulting lower tax digest has hurt many local governments, especially school systems. While city and county governments have more funding options and less state mandates, school systems have been saddled with more kids to teach, more mandates to meet, but with lower revenues and no alternative sources of funding.
• I hope to see more movies in 2015. To visit more friends. To get better organized. To hike a trail or two in the mountains. To go somewhere I've never been before. To see good art. To make a bucket list. To laugh, love and live with abandon.


Performs all necessary supervisory functions for subordinates; develops performance standards; trains and directs ongoing work.
Understands payroll and is able to manage time records and assist payroll clerk with any deficiencies.
Conducts physical inspections of kitchen preparation and storage areas to assure that sanitation standards are consistently attained and that areas are organized.
Assist with planning and delivering safety training including that related to proper storage and use of chemicals and maintains MSDSs in convenient location.
Performs all necessary supervisory functions for subordinates; develops performance standards; trains; directs ongoing work.
Requisitions china, glassware, etc., for the operation to ensure sufficient supplies for efficient service.
Work with Department Managers to order all necessary products.
Ensures all products are ordered and received correctly per department standards.
Manage, organize, clean & inventory of: produce walk-in; dairy walk-in; meat & seafood walk-in; and freezer walk-in.
Manage, clean, dry storage and dry storage area.
Liaison for all kitchen vendor issues.
Daily inventories of product in production.
Understands and balances work where necessary. i.e. kitchen production, line work, purchasing administrative, organization and cleaning.
Has proficient computer skills in word and excel.
for organizing and directing the processes necessary to provide breakfast, lunch, and snacks that meet child/adult care food program standards. Supervises kitchen staff and volunteers engaged in activities related to food preparation, production, and food service.
Push, pull or lift up to 50 to 80 pounds.