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Altosid Pro-G Mosquito Larvicide
10 Reviews | 19 Q&A
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Altosid Pro-G kills mosquitoes before they hatch and is simple and easy to use. Simply follow the directions and put the directed amount of granules in areas or in water where mosquitoes may breed. One container can treat over 5,000 square feet or up to 20,183 gallons of water. Altosid Pro-G will last up to 30 days.
Zoecon Altosid Pro-G Insect Growth Regulator releases effective levels of ALTOSID insect growth regulator for up to 30 days after application. Applications should be continued through out the entire season to maintain adequate control. Treated larvae continue to develop normally to the pupal stage where they die.
Altosid Pro-G Mosquito Larvicide – 10 oz
Altosid Pro-G Mosquito Larvicide – 2.5 Lb
Altosid Pro-G Mosquito Larvicide – Case (12 Jars)
Altosid Pro-G Mosquito Larvicide Label
Altosid Pro-G Mosquito Larvicide SDS
Supplemental Label
S-Methoprene 1.5%
Mosquitos, Mosquito Larvae
Bird Baths, Flowerpots, Ornamental Fountains, Gutters,Water Gardens, Rain Barrels, Pool Covers, Puddles, Horse Troughs and other animal drinking water receptacles
2.5 lbs treats up to 5,445 sq. ft. or 20,183 Gallons of Water
CT (Restricted To Licensed Applicators Only)
While mosquitoes may appear to be relatively innocent little creatures that occasionally bite and leave irritating itchy bumps on our arms and legs, they have recently become a more serious health concern to people and pets. With just one bite, mosquitoes can transmit any number of unpleasant (and some life-threatening) diseases, including the West Nile virus. They also have the capability to transmit heart worm disease to cats and dogs. Luckily, there are effective ways to reduce and control mosquito populations on and around your home property. Mosquitoes lay their eggs and in stagnant water such as bird baths, flowerpots, ornamental fountains, gutters, rain barrels, pool covers, puddles, horse troughs, and other water-holding receptacles. For greatest mosquito control, either eliminate or treat any stagnant bodies of water in receptacles or containers in your yard with Altosid Pro-G Mosquito Larvicide. Altosid Pro-G releases effective levels of the active ingredient Methoprene—a natural larvicide and IGR (insect growth regulator)—into the treated water source for up to 30 days after application. Exposed larvae will develop normally to the pupal stage, then die before becoming breeding, biting adults. Using the application rate table included with the product label (see duplication below), measure and then sprinkle the appropriate amount of Altosid Pro-G evenly over the surface of the water to be treated. To maintain optimal control, you should continue application of Altosid Pro-G every 30 days throughout the season. One 2.5 pound container of Altosid Pro-G will treat up to 5,445 square feet of water. When used according to the product label, this product will not cause any adverse affects in humans, animals, fish or vegetation.
GENERAL DIRECTIONS
Zoecon Altosid Pro-G Insect Growth Regulator releases effective levels of ALTOSID insect growth regulator for up to 30 days after application. Applications should be continued throughout the entire season to maintain adequate control. Treated larvae continue to develop normally to the pupal stage where they die. Apply Altosid Pro-G uniformly and repeat application as necessary.
Measure and then sprinkle the appropriate amount of Altosid Pro-G evenly over the surface of the water to be treated in order to control mosquitoes before they become breeding, biting adults. Measure the surface area of the water and follow the table below for the appropriate amount of product to use. Emptying or discarding small containers that are holding water will ensure the best results. In instances where small containers cannot be emptied or discarded and for locations such as flower pots, old tires, urns, etc. use approximately 80 granules for every gallon of water.
PRO–G APPLICATION RATE TABLE
Amount of Product per Treatment Area (surface area-less than 2 ft. deep of water)=
1 cup treats 2400 sq.ft.
1/4 cup treats 600 sq.ft.
1 tbsp. treats 150 sq. ft.
1/2 tbsp. treats 75 sq. ft.
1 tsp. treats 50 sq. ft.
1/2 tsp. treats 25 sq. ft.
NOTE: ALTOSID insect growth regulator has no effect on mosquitoes which have reached the pupal or adult stage prior to treatment.
APPLICATION TIME Apply Altosid Pro-G at any stage of larval mosquito development. Granules may be applied prior to flooding (i.e., "pre-hatch" or "pre-flood") in areas which flood intermittently. In such areas, one application of Altosid Pro-G can prevent adult mosquito emergence from several subsequent floodings. The actual length of control depends on the duration and frequency of flooding events.
APPLICATION SITES
Non-Crop Areas: Altosid Pro-G may be applied as directed above to temporary and permanent sites which support mosquito larval development. Examples of such sites include: snow pools, salt and tidal marshes, freshwater swamps and marshes (cattail, red cedar, white maple marshes), woodland pools and meadows, dredging spoil sites, drainage areas, ditches, wastewater treatment facilities, livestock runoff lagoons, retention ponds, harvested timber stacks, swales, storm water drainage areas, sewers, catch basins, tree holes, animal watering troughs, waterholding receptacles (e.g., tires, urns, flower pots, cans, and other containers), and other natural and manmade water-holding depressions.
Crop Areas: Altosid Pro-G may be applied as directed above to temporary and permanent sites which support mosquito larval development. Examples of such sites include: irrigated croplands, pastures, rangeland, vineyards, rice fields (domestic and wild), date palm, citrus, fruit, nut orchards, berry fields and bogs.
NOTE: Application of Altosid Pro-G to sites subject to water flow or exchange will diminish the product's effectiveness and may require higher application rates and/or more frequent applications.
See all 10 customer reviews »
By Barrie on 10/11/2014
This stuff is GREAT!!!! Use it in high grass, small puddles, and large pond. does not affect nature. FAST acting! AWESOME product!! mosquitos hatch w/in 3 days of raining, need to apply accordingly. lasts about 30 days!!! If you wait until mosquitos are hatching: you have decreased amt of mosquitos w/in 24 hours, almost none after 48 hours. Never found a better product. Using it for about 20 years.
Works great!!!
We start using this product at the beginning of the season, before the rain starts, to treat low spots that normally hold water and we continue treatment throughout the season and it keeps the mosquito population down. This product works great.
By Clara on 04/13/2011
Great product and easy to use. I used it in my back yard on the drainage area.
By Rick on 05/22/2014
We start using this product around the end of February beginning of March around the areas that hold water, even if they're dry, and keep treatment going through the season. So far as we've been using it, it's worked great.
Easy to use in cracks
By Maria on 07/03/2017
I got this to spread between the cracks in our deck where it gets very moist after a rain. I'm not sure if it has made a difference; we also sprayed with Talstar. But it was fairly easy to do, so worth a shot.
See all 10 customer reviews
Is Altosid Pro-G Mosquito Larvicide safe for bodies of water that contain fish?
According to Zoecon the manufacturer of Altosid Pro-G Mosquito Larvicide, this product is safe to use in bodies of water that contain fish.
Is Altosid harmful to my dog if she drinks standing water where Altosid has been applied?
Altosid can be applied safely to bird baths, flowerpots, ornamental fountains, gutters, water gardens, rain barrels, pool covers, puddles, horse troughs and other animal drinking water receptacles so it should not be harmful to your dog.
Treat irrigated horse pastures for misquitos
3 acres in Arizona Terrible mosquito problem after irrigating or rain Horses eat the grass
Altosid® Pro-G may be applied as directed to temporary and permanent sites which support mosquito larval development such as irrigated horse pastures. Altosid Pro-G releases effective levels of the active ingredient Methoprene—a natural larvicide and IGR (insect growth regulator)—into the treated water source for up to 30 days after application. Exposed larvae will develop normally to the pupal stage, then die before becoming breeding, biting adults. Using the application rate table included with the product label, measure and then sprinkle the appropriate amount of Altosid Pro-G evenly over the surface of the water to be treated. To maintain optimal control, you should continue application of Altosid Pro-G every 30 days throughout the season. One 2.5 pound container of Altosid Pro-G will treat up to 5,445 square feet of water. When used according to the product label, this product will not cause any adverse affects in humans, animals, fish or vegetation.
9 of 10 people found this answer helpful
What is the shelf life of Altosid Pro-G Mosquito Larvicide?
The shelf life of Altosid Pro-G Mosquito Larvicide is three years from the date of purchase. This product can be applied as soon as every 30 days.
How much Altosid Pro-G do I need to use and how often for a septic tank?
Altosid Pro-G releases effective levels of ALTOSID insect growth regulator for up to 30 days after application. Applications should be continued throughout the entire season to maintain adequate control. Treated larvae continue to develop normally to the pupal stage where they die. The amount of product you use will be based solely on the amount of water you are treating. Please refer to the product label for more information. Usually mosquito larvae do not develop in septic tanks, so please be sure you have properly identified the insects you are seeking to control.
Altosid Pro-G Mosquito Larvicide Rating: 4.8 (10 Reviews / 19 Q&A)


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\subsection{Pivot Actions}
\begin{figure*}
    \centering
    \includegraphics[width=\textwidth]{figures/pipeline.pdf}
    \caption{The process of collecting pivot actions.}
    \label{fig-pipeline}

\end{figure*}
Pivot actions are actions that differ between a dish pair, like \emph{blanch} and \emph{make diagonal cuts} in the case of \emph{<red-braised pork, red-braised crucian carp>}. Since there is no gold standard for the modified recipes, we evaluate the quality of the modified recipes by collecting the pivot actions.
For the dish pair \emph{<$d_b$, $d_t$>}, the pivot action set, $\mathcal{P}$, contains both actions to remove, $\mathcal{P}_R$, and actions to insert, $\mathcal{P}_I$.
$\mathcal{P}_R$ are actions that may appear in the recipes of $d_b$ but are \emph{not} appropriate for $d_t$; $\mathcal{P}_I$ are actions that are not needed for $d_b$ but \emph{should} appear in the recipes of $d_t$. In the example of Figure~\ref{fig-task}, \emph{blanch the pork} belongs to $\mathcal{P}_R$ and \emph{make diagonal cuts} belongs to $\mathcal{P}_I$.

It is hard to ask annotators to write pivot actions from scratch, and checking all the actions in the recipes is inefficient. We observe that actions that frequently occur in the recipes of a dish are more likely to be necessary for the dish. Taking advantage of the abundant recipes of each dish in \textsc{XiaChuFang}, we categorize actions based on frequency, and ask annotators to annotate the vague ones. Figure~\ref{fig-pipeline} shows the semi-automatic pivot action collection workflow.

\paragraph{Recipe Parsing.}
We first parse the recipe into a list of actions. An action $(v, igs, tools)$ is centered on a verb, often accompanied by ingredients and cooking tools. The definition is consistent with previous recipe processing works~\citep{donatelli2021aligning}, and the parsing details are in Appendix~\ref{appendix:parsing}.

\paragraph{Pilot Study.}
\begin{figure}
    \centering
    \includegraphics[width=\columnwidth]{figures/pilot.pdf}
    \caption{Pilot annotation results.}
    \label{fig-pilot}

\end{figure}
We conduct a pilot annotation to verify the validity of categorizing actions according to frequency. We randomly select 10 dish pairs to check whether annotators agree with the automatic categorizing results.

For each action $a$, we calculate its frequencies, $f_b$ and $f_t$, according to
its appearances in the recipes of $d_b$ and $d_t$, respectively, and categorize it as:

$\mathcal{P}_R$ (actions to remove), if $f_b>\alpha \times f_t$ and $f_t<\tau_R$ (here $\alpha$ is a coefficient greater than 1), the criteria means that the action appears much often in recipes of $d_b$, and rarely appears in recipes of $d_t$;

$\mathcal{P}_I$ (actions to insert), if $f_t>\alpha \times f_b$ and $f_t>\tau_I$, which means that the action appears frequently in recipes of $d_t$, and rarely in recipes of $d_b$;

$\mathcal{M}$ (actions to manually check): if $a$ satisfies $f_b>\alpha \times f_t$ or $f_t>\alpha \times f_b$, but does not satisfy the threshold constraints ($f_t$ is not low or high enough).

We set $\alpha=5, \tau_R=0.01, \tau_I=0.1$ empirically, and ask annotators to annotate all actions in $\mathcal{P}_R \cup \mathcal{P}_I \cup \mathcal{M}$. We give annotators three instances of each action, and request them to determine whether the action \emph{does not occur, rarely occur, sometimes occur}, or \emph{always occur} in the target dish.

Annotation results are shown in Figure~\ref{fig-pilot}. We collect 3.4 answers for each question on average, and perform a majority vote (more than half of the annotators agree with it).
More than 2/3 actions in $\mathcal{P}_R$ are annotated as \emph{do not occur}, and more than 2/3 actions in $\mathcal{P}_I$ are annotated as \emph{always occur}. After a careful check over those disagreement cases (in Appendix~\ref{appendix:disagreement}), we find that the disagreements are mainly due to the subjectivity of cooking.
Actions in $\mathcal{P}_R$ are more likely to be annotated as \emph{do not occur}, and actions in $\mathcal{P}_I$ are more likely to be annotated as \emph{always occur}, comparing to actions in $\mathcal{M}$. These indicate the validity of distinguishing the automatically mined actions ($\mathcal{P}_R$ and $\mathcal{P}_I$) and those remaining to be manually checked.

\paragraph{Human Annotation.}
We then perform human annotation on the set $\mathcal{M}$ of all dish pairs. Each question is annotated by at least three annotators. All annotators have more than one year of culinary experience, and a questionnaire sample is in Appendix~\ref{appendix:annotation}.

We add actions with the majority vote \emph{do not occur} into $\mathcal{P}_R$, actions with the majority vote \emph{always occur} into $\mathcal{P}_I$, and discard the others.
A dish pair has 22.4 pivot actions on average: 13.6 to remove and 8.8 to insert.

\subsection{Action Location}
The order of actions is not arbitrary, as actions affect ingredient states such as shape and temperature~\citep{bosselut2018simulating}. Therefore, models are expected to not only generate the necessary actions, but also locate them in the right place.
We judge the correctness of the position according to the order constraints, i.e., examining which actions should appear before or after the inserted action.
That is, for each action $a$ to insert, it should be placed after actions $\mathcal{A}_p^a$ and before actions $\mathcal{A}_s^a$. The order constraints can be seen as causal dependencies~\citep{pan2020multi}: actions in $\mathcal{A}_p^a$ are causes of $a$, and actions in $\mathcal{A}_s^a$ are effects of $a$.

Previous works use pre-defined action graph or train a classifier to recognize the relationship between actions~\citep{mori2014flow,pan2020multi}. Thanks to the plentiful recipes of each dish in our dataset, we are able to identify the causal dependencies between actions using causal estimation techniques.
We regard the pivot action $a$ as the \emph{effect}, and exhaustively take every other action $a'$ that appears before $a$ in the recipes of $d_t$ as the \emph{cause}. All other actions that appear before $a$ and $a'$ are considered as potential confounders. We estimate the averaged causal effect $\psi_{a', a}$ with propensity score matching~\citep{rosenbaum1983central}. If $\psi_{a', a}>\tau_c$,
we put $a'$ into $\mathcal{A}_p^a$. We set $\tau_c=0.1$ empirically, and construct $\mathcal{A}_s^a$ in the same way: setting $a$ as the cause and other actions as the effect.
\section{Analysis}
\subsection{Human Evaluation}
We conduct a human evaluation to assess model performance more comprehensively, and to check whether our evaluation results are consistent with human impressions.
We randomly select one instance for each dish pair, and ask raters to choose the best and worst generated recipes on \emph{grammaticality} (whether the text is grammatically correct and fluent), \emph{correctness} (whether the text is a correct recipe of the target dish), and \emph{preservation} (whether the text preserves the base recipe's cooking and writing style).

Expert-written recipes are also added into comparison (referred to as \emph{Expert}).
We ask cooking experts, who have cooked for more than a decade, to write counterfactual recipes for the 50 base recipes. As GPT-2 (D) and GPT-2 (D+R) perform comparably in previous evaluations, we only keep GPT-2 (D) as a baseline of directly using the PLM.

We obtain the ratings with Best-Worst Scaling~\cite[BWS,][]{louviere1991best}: the percentage of a model being selected as the best minus the percentage of being selected as the worst. The score ranges from -100 to 100, and the higher is better. BWS is shown to be more reliable than Likert scales and is used in previous NLP human evaluations~\citep{kiritchenko2017best}. Each recipe is rated by at least two people.

\input{tables/human}
Results are shown in Table~\ref{table-human}.
\emph{Expert} is much better than all three models in all three aspects, indicating there is a big gap between the counterfactual recipe generation capabilities of models and humans.
Among the three models, GPT-2 (D) is better in the \emph{correctness} of cooking, which is consistent with the soft action-level evaluation results. Based on the same PLM, models' performance hurt when they try to go closer to the style of the base recipe.
\textsc{EduCat} is better in the \emph{preservation} of the base recipe as it modifies word by word on the base recipe. This is also consistent with the preliminary preservation results.
\textsc{Delorean} is in the middle regarding \emph{correctness} and \emph{preservation}, but performs the worst in \emph{grammaticality}. Considering its high ingredient coverage ratio, it achieves surface-level composition of the new ingredient and the base recipe, but the composition is rigid and incoherent.
We also conduct case studies analyzing model mistakes in Appendix~\ref{appendix:case}.

\subsection{Challenges}
We discuss challenges of learning culinary knowledge and composing them in the recipe scenario.

\paragraph{Various mentions of ingredients.} An ingredient may have different names in Chinese recipes, like \begin{CJK*}{UTF8}{gbsn}鸡爪\end{CJK*} (chicken feet) is also called \begin{CJK*}{UTF8}{gbsn}鸡脚\end{CJK*} or \begin{CJK*}{UTF8}{gbsn}凤爪\end{CJK*}. Moreover, the mentions of ingredients may change when their status change, like \begin{CJK*}{UTF8}{gbsn}猪肉\end{CJK*} (pork) converts to \begin{CJK*}{UTF8}{gbsn}肉丝\end{CJK*} (shredded pork) after cut. We sample 20 recipes from the recipe corpus, and find the variation of mentions is quite common: the main ingredient has 2.1 different mentions on average. This adds to the difficulty of identifying and tracking ingredients, which is involved in the L1 evaluation.

\paragraph{Omission of ingredients in recipes.} Object omission is commonly seen in Chinese~\citep{huang1998logical}, especially in informal writings like recipes. For example, the object ingredient \emph{squid} appears only once at the beginning in the sequence of actions \begin{CJK*}{UTF8}{gbsn}洗净鱿鱼,去除中间墨囊,切成块。焯过捞起沥水。\end{CJK*} (\emph{Wash the squid, remove the middle ink sac, cut into pieces. Blanch and drain.})
Each sampled recipe has an average of 3.3 actions where the ingredient is omitted, which increases the difficulty of linking actions with ingredients and may affect the L2 performance.

\paragraph{Variety of cooking styles.} Different people have different cooking styles, so the recipes they post for the same dish also vary. For example, when cooking \emph{stir-fried shredded potatoes}, some people \emph{blanch} potatoes to make them crispier, while others may prefer the soft texture. Models may be confused by the varying cooking ways, and this poses new challenge in compositional generalization.

\paragraph{Order determination.} As recipes are sequential and order-sensitive, models need to find the appropriate location when inserting an action. Learning the order knowledge is not trivial, which requires models to separate \emph{sequential} actions from \emph{parallel} ones and find the cause-and-effect relationship between actions.

\section*{Appendix}
\input{tables/eval_appendix}
\input{tables/case}
\section{Implementation Details}
\subsection{Dish Pairs Selection}
\label{appendix:dish_pairs}
\input{tables/dish_pairs_full}
We provide the full list of dish pairs in Table~\ref{table-dish-full}. The 50 dish pairs involve appetizers, main dishes, soups, staples, and desserts. Both the translated English version and the original Chinese version are provided.

We choose the dish pairs based on the following criteria:

1. The dishes are common in Chinese cuisine. Each dish has at least 50 recipes in our \textsc{XiaChuFang} dataset.

2. Recipes of the dishes have not been seen by the PLMs. We use CLUECorpus2020, an open-sourced large-scale Chinese corpus, as the pretraining corpus of PLMs, and verify that none of the recipes for these dishes are in the corpus.

3. Models have the opportunity to learn about the ingredients and flavors. We ensure that for each ingredient or flavor appearing in the dish pairs, there are at least 5 other dishes in \textsc{XiaChuFang} that share the same ingredient or flavor.

\subsection{Recipe Parsing}
\label{appendix:parsing}
As we did not find off-the-shelf Chinese recipe parsing models, we parse recipes from scratch. The parsing process converts a recipe into a list of clustered actions.

\paragraph{Glossary} We build a glossary of common verbs, ingredients, and tools; and cluster them into 92 verb classes $V$, 476 ingredient classes $I$, and 20 tool classes $T$. This is to tackle the \emph{multiple surface forms} of the same or similar meaning.
For example, \{\begin{CJK*}{UTF8}{gbsn}
加入, 放入, 添加, 加, 下, ...
\end{CJK*}\}
all mean \emph{add} in Chinese recipes.
The classes are clustered by K-means~\citep{macqueen1967some} of word embeddings and adjusted by experts.

\paragraph{Dependency Parsing} We perform dependency parsing on recipes, and build actions from verbs in the dependency parsing tree. If a verb has noun children that are ingredients or cooking tools, they are also included in the action.
For example, as shown in Figure~\ref{fig-pipeline}, the phrase \emph{cut off the head} is parsed into the action $(cut, head, \varnothing)$.
As we aim to collect common patterns, we only consider words in the glossary and filter out the rare ones.

\paragraph{Proto-actions} The actions are grouped by word classes: if two actions' verbs are in one verb class, and the classes of their ingredients and tools are the same, they are clustered into one proto-action
\begin{equation}
    a=(v, igs, tools),
\end{equation}
where $v \in V$, $igs \subseteq I$, and $tools \subseteq T$.
Therefore, a recipe is converted into a list of proto-actions. A recipe is made up of 19.6 proto-actions on average.

\paragraph{Limitation} The parsing is quite coarse as we do not have fine-grained parsed Chinese recipes for model training. For example, we do not consider the attribute knowledge: like the cooking temperature and the amount of ingredients. We plan to annotate recipes and build accurate parsing tools in the future.

\subsection{Annotation Samples}
\label{appendix:annotation}
\begin{figure*}[ht]
    \centering
    \begin{subfigure}[t]{0.48\textwidth}
        \centering
        \includegraphics[width=\columnwidth]{figures/annotation1_zh.jpg}
        \caption{Chinese version.}
    \end{subfigure}%
    ~
    \begin{subfigure}[t]{0.48\textwidth}
        \centering
        \includegraphics[width=\columnwidth]{figures/annotation1_en.jpg}
        \caption{Translated English version.}
    \end{subfigure}
    \caption{Annotation sample of pivot action collection.}
    \label{fig-annotation1}
\end{figure*}
\begin{figure*}[!ht]
    \centering
    \begin{subfigure}[t]{0.48\textwidth}
        \centering
        \includegraphics[width=\columnwidth]{figures/annotation2_zh.jpg}
        \caption{Chinese version.}
    \end{subfigure}%
    ~
    \begin{subfigure}[t]{0.48\textwidth}
        \centering
        \includegraphics[width=\columnwidth]{figures/annotation2_en.jpg}
        \caption{Translated English version.}
    \end{subfigure}
    \caption{Annotation sample of manually counterfactual writing.}
    \label{fig-annotation2}
\end{figure*}
We demonstrate an annotation sample of pivot action collection in Figure~\ref{fig-annotation1}, and a counterfactual writing sample in Figure~\ref{fig-annotation2}.

\subsection{Computing Infrastructure}
Experiments are conducted on NVIDIA GeForce RTX 3090 GPU. It takes \textsc{EduCat} two minutes to generate a recipe on average, and no more than half a minute for other models. The parameter size of the GPT-2 we used is 102M.

\section{Additional Results}
\subsection{Disagreement Analysis of Pilot Study}
\label{appendix:disagreement}
We examine the 31 disagreement actions in $P_R$ and $P_I$ and find that the main reason is that the cooking process is subjective, especially in informal circumstances like home cooking. People will change some auxiliary ingredients or steps according to their personal preferences, but the principal steps and ingredients are highly consistent. For example, some people add sugar when cooking spicy stir-fried squid, while some never do it. In the 31 disagreements, 68\% are related to the auxiliary ingredients. We think the disagreements would not affect our main findings as we care more about the steps related to principal ingredients.

\subsection{Action-level Evaluation Results}
We do not place the $\mathbf{F_1^+}$ and $\mathbf{F_1^-}$ of hard action-level evaluation in Table~\ref{table-action} due to space limit. The full results are provided in Table~\ref{table-eval-appendix}.

\section{Case Study}
\label{appendix:case}
We demonstrate generated recipes of two dish pairs in Table~\ref{table-case} to analyze some common mistakes of counterfactual generation models. We only show part of the text for the ease of reading, and the full text are in Table~\ref{table-case-full-1} and Table~\ref{table-case-full-2}.

\textbf{Failing to modify the ingredient.} \textsc{EduCat} has difficulty in generating the newly added ingredient and removing the replaced one. In the case of \emph{steamed hairy crab}, it follows the base recipe to \emph{clean the turbot}, overlooking the new ingredient \emph{hairy crab} in the target dish; and it still uses the ingredient \emph{squid} in the base dish when cooking \emph{spicy stir-fried snails}. These illustrate the instability of \textsc{EduCat} in surface-level composition.

\textbf{Failing to remove irrelevant actions.} In the case of \emph{steamed hairy crab}, \textsc{EduCat} keeps the action \emph{make a few cuts on both sides}, which is not appropriate for a hairy crab as it has a hard shell; and in the case of \emph{spicy stir-fried snails}, it does not delete \emph{remove the outer skin}, but snails have hard shells. These show that the model does not learn the relationship between the actions and ingredients in the finetuning stage.

\textbf{Failing to generate necessary actions.} When cooking snails, we should let them \emph{spit out the sediment} before putting them in the pot, but \textsc{Delorean} does not generate the action. The action \emph{blanch} can remove the fishy smell from aquatic products, and is suitable for both squid and snails, but \textsc{Delorean} wrongly deletes it. It does not understand the purpose of these actions in this case.

\textbf{Wrong order.} \textsc{Delorean} begins the recipe of \emph{steamed hairy crab} with \emph{lift the crab shell}. This is an appropriate action for this dish, but should not happen at the beginning when the crab is raw. Models should learn the underlying causal dependency of actions to avoid this kind of mistake.

\input{tables/case_full}

\section{Conclusion}
We propose counterfactual recipe generation, a task to evaluate models' compositional generalization ability in realistic settings. We explore two levels of compositional competencies: modifying the changing ingredient, and modifying the ingredient-related actions in the base recipe. Experimental results show that current models have difficulties in composing the new ingredient and actions into the base recipe while preserving the cooking and writing styles. By proposing this task, we reveal the large gap between current pretrained language models and humans in the deep understanding of procedural text, and want to elicit attention to pretrained language models' abilities in unsupervisedly learning and generalizing in real-life scenarios.
\section{Data Preparation}
\subsection{The \textsc{XiaChuFang} Recipe Dataset}
We collect a novel Chinese dataset \textsc{XiaChuFang} of 1,550,151 recipes from \url{xiachufang.com}, a popular Chinese recipe sharing website. Compared to the commonly used English recipe dataset Recipe1M+, \textsc{XiaChuFang} contains 1.5 times the recipes.
The website provides a list of common dishes. We map the recipe titles to these dishes, and find 1,242,206 recipes belonging to 30,060 dishes. A dish has 41.3 recipes on average. The average length of a recipe is 224 characters. Organizing recipes in terms of dishes helps us to learn what different people have in common when cooking this dish, which are often the necessary actions.

\input{tables/dish_pairs}
We select 50 dish pairs \emph{<$d_b$ (base dish), $d_t$ (target dish)>} for evaluation. The two dishes share the same flavor and differ in one principal ingredient in the dish name.~\footnote{Some auxiliary ingredients may also change accordingly, but we only focus on the changes directly associated with the principal ingredient changed in the dish name.}
We randomly sample 50 recipes of each base dish as the base recipes, and form 2,500 evaluation instances in total. By letting models modify different recipes for the same dish, we better measure whether models have actually learned the culinary knowledge and can apply the knowledge flexibly.

Table~\ref{table-dish} shows several dish pairs used in our evaluation, and the full list is in Appendix~\ref{appendix:dish_pairs}.
In the first three lines of Table~\ref{table-dish}, the target dish replaces one ingredient of the base dish; and in the last two lines, one ingredient is added to the target dish.
The latter situation is rare in the dataset (only 8\%), but it presents additional challenges, as simply substitution in recipes does not work, and models have to generate reasonable actions to add the new ingredient in the right place.

We regard the recipes that do not belong to the 50 dish pairs as the \emph{recipe corpus}. The corpus size is 1,479,764. We expect models to learn cooking knowledge unsupervisedly from this corpus.

The chosen dish pairs meet the following criteria: they are common in Chinese cuisine; recipes of the dishes have not been seen by the PLMs we used; and models have the opportunity to learn about the ingredients and flavors from the \emph{recipe corpus}. For example, for the ingredient \emph{crayfish}, models can learn how to process it from recipes of \emph{stir-fried crayfish}, \emph{garlic crayfish}, \emph{chilled crayfish}, etc. Details of the selection criteria are in Appendix~\ref{appendix:dish_pairs}.
\section*{Ethics Statement}
\paragraph{Intellectual Property.} We ensure that intellectual property of the original authors of recipes in \textsc{XiaChuFang} is respected during data collection with permission of licence\footnote{\url{https://www.xiachufang.com/principle/}}. And the collected data would not be used
commercially.

\section{Evaluation}
\input{tables/eval}
\subsection{L1: Surface-level Evaluation}
We first evaluate the generated texts from two aspects, to answer the L1 question: Can models incorporate the changing ingredient into the base recipe without making excessive changes?
\subsubsection{Evaluation Metrics}

\paragraph{Coverage of Ingredients.}
We check the coverage ratio of newly added ingredients in the generated recipes. For instances where an old ingredient is replaced, we also check whether the replaced ingredient still exists in the generated recipes:
\begin{equation}
    CoI=\frac{\sum_{i=1}^{N} \textbf{1}[ing_i \in r_i]}{N},
\end{equation}
where $ing_i$ is the added/replaced ingredient in the $i$-th target dish , and $r_i$ is the generated recipe. We expect the coverage ratio of the added ingredient to be high, and the coverage ratio of the replaced ingredient to be low.
The metric only captures if the ingredient appears in the recipe, but does not check whether the ingredient is processed correctly, which will be examined in the action-level evaluation.

\paragraph{Extent of Preservation.}
We calculate the correspondence between the generated recipe and the base recipe with BLEU~\citep{papineni2002bleu} and BERTScore~\citep{bert-score}.
The correspondence is not expected to be 100\% as one major ingredient changes, but we expect the score not to be too low, as it indicates better preservation.

\subsubsection{Results}
\label{sec:l1_results}
From Table~\ref{table-preliminary}, we observe that \textsc{Delorean} successfully covers most of the added ingredients and removes most of the replaced ingredients, benefiting from its decoding strategy that starts from the target dish and pushes the generated text to be coherent with the target dish in the whole generation.
GPT-2 (D) exhibits the generation ability of finetuned PLMs. Without the disturbance of the base recipe, it will not generate the replaced ingredient, but it still misses the added ingredient in several cases.
By adding the task instruction and the base recipe into the input, GPT-2 (D+R) underperforms GPT-2 (D) on both added and replaced ingredients, indicating the model may not be able to fully understand the instruction and is interfered by the base recipe.
\textsc{EduCat} does not perform well in ingredient coverage, failing to insert many ingredients in the target dish and still keeping many replaced ingredients. It makes many unnecessary modifications but fails to detect the true contradictory between the target dish and the recipe.

On the other hand, it is difficult for all models to preserve the style of the base recipe. The BLEU scores of GPT-2 (D), GPT-2 (D+R), and \textsc{Delorean} are less than 5\%. As a reference for assessment, we ask human experts to conduct the counterfactual writing task (the recipes they write are further used in the human evaluation), and the BLEU score between expert-written recipes and the base recipes is 65.2\%. Compared with human experts, only \textsc{EduCat} well preserves the original style, and the other models perform poorly in preservation.
Although \textsc{Delorean} constrains the generated text to be similar to the base recipe, the constraint is still weak and it actually modifies most of the base recipes, as its correspondence with the base recipe is on par with GPT-2 (D).

These results show that existing models still have difficulties in the basic composition. They cannot decompose the replaced ingredient or compose the added ingredient into the recipe well, indicating the deficiency in identifying and tracking an ingredient.

\subsection{L2: Action-level Evaluation}
We design action-level evaluation metrics to answer the L2 question: Can models identify pivot actions when the ingredient changes, remove the inappropriate actions and compose the necessary actions into the base recipe?

\subsubsection{Evaluation Metrics}
We measure the precision, recall, and $F_1$ score between the changes, $\mathcal{C}$, made by a model and the pivot actions, $\mathcal{P}$. $\mathcal{C}$ is constructed by parsing both the base and generated recipes, and finding the different actions. For inserted actions in $\mathcal{C}\cap\mathcal{P}_I$, we additionally examine their positions in the generated recipe, and only those that meet the order constraints are kept in the intersection.

Considering different expressions may have the same or similar meaning, we also design a \emph{soft} version measurement (and those in the above paragraph are regarded as \emph{hard}). In the soft version, we add actions of similar surface forms $sim(ph(a), ph(a'))>\tau_s$ into the intersection, using the similarity score as their weight (the weight of actions in the hard intersection is always 1).
$ph(a)$ is the text phrase corresponding to action $a$, $a \in \mathcal{C}$ and $a' \in \mathcal{P}$. We calculate $sim(\cdot)$ with cosine similarity of averaged word embeddings, and set $\tau_s$ to 0.9.

\subsubsection{Results}
From Table~\ref{table-action}, we find that the action-level performance is quite low: all models achieve no more than 20\% hard $F_1$ and no more than 30\% soft $F_1$.
\textsc{EduCat} performs the worst, probably because it modifies the base recipe word by word. Even if the finetuned PLM learns certain pivot actions, it is still hard for \textsc{EduCat} to add or delete the entire action as it may need several modification steps.
Comparing with the editing-based method, \textsc{Delorean} performs relatively better, as it considers the generated recipe and the new ingredient from a holistic perspective. This makes it easier to find the actions that are consistent or contradictory with the target dish, and adds/removes an action at once.
The performance of \textsc{Delorean} and the two ways of directly using GPT-2 are similar, as \textsc{Delorean} changes most of the base recipe.

Inserting necessary actions is harder than removing inappropriate actions for all models. Insertion requires models to link unseen actions with the ingredients, and locate the actions in right places.
GPT-2 (D) locates almost all of the inserted actions correctly
(99\% of them meet the order constraints). The order accuracy is 87\% for GPT-2 (D+R), 84\% for \textsc{EduCat} and 80\% for \textsc{Delorean}. Compared with generation from scratch, the difficulty of action positioning raises when models are asked to rewrite on the base recipe. Models learn the order of actions from the finetuning recipe corpus with the causal language modeling objective, so it is more natural to generate actions sequentially.
\section{Introduction}
Reading is an effective way to gain knowledge. When people read, mental processes like structured information extraction and rule discovery go on in our brains~\citep{gibson1975psychology}.
In the case of cooking, we read recipes of various dishes, gain knowledge of ingredients and flavors, and compose them to cook other dishes.~\footnote{We provide explanations of concepts related to recipes in Table~\ref{table-terminology} for better understanding.}

This process involves knowledge acquisition and composition. As shown in Figure~\ref{fig-intro}, when people read recipes, they distill the knowledge of flavors and ingredients, like \emph{soy sauce is usually used to get red-braised flavor} and \emph{people often make diagonal cuts on fish to better marinate}.
People can then cook new dishes like \emph{red-braised crucian carp} by composing existing knowledge about how to form the \emph{red-braised} flavor and how to cook \emph{fish}.

\begin{figure}
    \centering
    \includegraphics[width=0.9\columnwidth]{figures/intro.pdf}
    \caption{An example of the recipe learning process of human for the dish \emph{red-braised crucian carp}.}
    \label{fig-intro}

\end{figure}
\begin{figure*}
    \centering
    \includegraphics[width=\textwidth]{figures/task.pdf}
    \caption{The counterfactual recipe generation task and the two levels of compositional competencies examined.}
    \label{fig-task}

\end{figure*}
We expect models to acquire culinary knowledge unsupervisedly, and be able to use the knowledge skillfully, e.g., composing new dishes.
Current recipe processing tasks do not examine this ability explicitly. Recipe understanding tasks usually evaluate the models in a specific aspect under supervision, like identifying ingredient states or relationship between actions.
Recipe generation tasks evaluate whether models can generate fluent recipes, but do not investigate whether the generation ability relies on simple word correlation or culinary knowledge composition.

Current models exhibit strong compositional generalization ability in the understanding of synthetic texts~\citep{lake2019compositional,nye2020learning, weissenhorn2022compositional}, but few of them conduct experiments on realistic data, which is more challenging in two ways: (1) there are far many synonymous expressions in real-world texts, like various mentions of ingredients and actions in recipes~\citep{fang2022does}, but synthetic data often use a single expression for one meaning~\citep{keysers2019measuring}; (2) the knowledge in synthetic data is often expressed accurately and clearly, but knowledge in data provided by real users is varying. For example, when forming the \emph{red-braised} flavor, most people use soy sauce while few use iced sugar instead. Moreover, previous compositional generalization tasks mainly focus on semantic parsing and language grounding, while we aim to examine models in the form of natural language generation.

We propose the \textbf{Counterfactual Recipe Generation} task, to examine models' compositional generalization ability in understanding and generating recipe texts.
This task requires models to answer the question: given a recipe of a dish, how will the recipe change if we replace or add one ingredient?
As shown in Figure~\ref{fig-task}, models are asked to modify the base recipe of \emph{red-braised pork} to form a new recipe of \emph{red-braised crucian carp} while preserving the original cooking and writing style.

We develop baseline methods to solve the task, as existing compositional generalization models cannot fit in the counterfactual generation task form. We finetune pretrained language models (PLMs) on our collected Chinese recipe corpus to learn culinary knowledge, and use prevalent unsupervised counterfactual generation frameworks to generate counterfactual recipes given \emph{\{base dish, base recipe, target dish\}}, where the target dish differs from the base dish in a main ingredient.

Instead of annotating gold target recipes, we evaluate models in two levels of compositional competencies, which is less labor-consuming.
The surface level (L1) is the fusion of the \textbf{changing ingredient} and the base recipe: the new ingredient should be added to the recipe, and the replaced ingredient should be removed. For instance, the original step \emph{wash the pork} needs to be changed to \emph{wash the crucian carp}. We evaluate the ingredient coverage ratio of the added and replaced ingredient, and the degree of recipe modification. Results show that existing PLMs can hardly cover the new ingredient and delete the replaced ingredient without making unnecessary modifications to the base recipe.

The deeper level (L2) is the fusion of \textbf{actions related to the changing ingredient} and the base recipe: actions to process the new ingredient should be inserted, and actions only related to the replaced ingredient should be deleted. This is a much harder problem involving decompositions and compositions of actions. In the case of Figure~\ref{fig-task}, a model should know \emph{blanch} is related to pork and is not suitable for a crucian carp, in order to delete the action \emph{blanch the pork}. Also, the action \emph{make diagonal cuts} is widely used in fish dishes, and should be added to the recipe. Our action-level evaluations show that most models fail to either remove all the irrelevant actions, or insert all the necessary ones, indicating that current PLMs have not fully learned the patterns of ingredient processing.

Our main contributions are as follows:
1) We propose the counterfactual recipe generation task to test models' compositional generalization ability in a realistic scenario.
2) We collect a large-scale Chinese recipe dataset and build a counterfactual recipe generation testbed with fine-grained action-level annotations, which can also increase the research diversity for procedural text understanding.
3) We examine  models' compositional generalization ability from two levels. Our experiments show current PLMs are unable to modify the ingredient and preserve the original text style simultaneously, and will miss actions related to the changing ingredient that need to be adjusted.
Further analysis reveals that current models are still far from human experts in the deep understanding of procedural texts, like tracking entities and learning implicit patterns.

\input{tables/terminology}
\section*{Limitations}

\paragraph{Scalability to datasets of other languages.} In this paper, we only consider common Chinese dishes which merely take up a small portion of those across the world. Our counterfactual recipe generation task can be extended to datasets of other languages and other cuisines, and it is promising to take the geo-diversity~\cite{yin2021broaden} of cooking knowledge into account. For example, \emph{<beef stew, venison stew>} is a potential dish pair in Irish cuisine, and the pivot actions of \emph{venison stew}
are different from Chinese cooking. At the practical level, we make use of various recipes of each dish in pivot action collection and action location. So the dataset needs to contain a certain number of recipes for each evaluated dish, in order to measure the necessities of actions with their frequencies.

\paragraph{Alignment of multiple surface forms.} There are various expressions that have the same meaning in human-written recipes. We use soft action-level measurements to tackle the problem, but more human involvement may be needed to align the expressions accurately. This also limits the evaluation of ingredient coverage. We only consider the ingredient name that appears in the dish name, as we are unable to take all mentions of the ingredient into consideration.

\paragraph{Imperfection of evaluation metrics.} It is hard to precisely evaluate generated text in natural language generation tasks, and our task also faces this challenge. We only consider some aspects to evaluate the counterfactually generated recipe, including the coverage of ingredients, the correspondence with the base recipe, and the coverage of pivot actions. Other aspects like the discourse structure of the recipe can also be taken into account.

\paragraph{Lack of corresponding solutions.} Although we show that current models' compositional generalization abilities are relatively poor in the recipe generation scenario, we did not propose solutions for improvement. Facing the challenges we discussed, we intend to design corresponding methods in the future.
\section*{Acknowledgments}
This work is supported in part by NSFC (62161160339) and National Key R\&D Program of China (No. 2020AAA0106600). We would like to thank the anonymous reviewers for the helpful suggestions, and our great annotators for their careful work, especially Yujing Han, Yongning Dai, Meng Zhang, Yanhong Bai, Li Ju, Siying Li, and Jie Feng.  Also, we should thank Chen Zhang, Da Yin, Quzhe Huang and Nan Hu for their detailed comments. For any correspondence, please contact Yansong Feng.

\section{Baseline Methods}
We use two competitive unsupervised counterfactual generation  models~\citep{qin2020back,chen2021unsupervised} as our baselines. We do not consider existing compositional generalization models as it is hard to adapt them to our task setup. In order to make those models learn culinary knowledge, we finetune GPT-2 on the recipe corpus and use it as the backbone of the baselines.
\subsection{Learn from Recipes}
\label{sec:finetune}
There are various ways to exploit the recipe corpus. We take a common practice in our baseline method: following the pretraining objective to finetune the model.
Specifically, we use GPT-2~\citep{radford2019language} pretrained on CLUECorpus2020~\citep{xu2020cluecorpus2020,zhao2019uer}. We concatenate the dish name and the recipe as the input: \emph{[Dish Name] is made as follows. [Recipe Text].}, and finetune GPT-2 with the causal language modeling objective: predicting the next word given all the previous ones. We finetune GPT-2 with learning rate 5e-5 for 10 epochs.

\subsection{Perform Counterfactual Generation}
Existing unsupervised counterfactual generation methods use off-the-shelf PLMs with no extra supervision, and perform well on the TimeTravel~\citep{qin2019counterfactual} dataset. Among them, \textsc{EduCat}~\citep{chen2021unsupervised} and D\textsc{elorean}~\citep{qin2020back} are two representative works and they differ in the modification style: \textsc{EduCat} edits locally and explicitly, while \textsc{Delorean} refines the text implicitly from a holistic view.
Besides the two models, we also take two ways to directly utilize GPT-2 for comparison.

\textbf{GPT-2 (D)}\quad In the basic setting, we ask GPT-2 to predict after \emph{[Target Dish] is made as follows.} without considering the base recipe. This is consistent with the finetuning setting, and can exhibit the model's recipe generation ability without being affected by the base recipe.

\textbf{GPT-2 (D+R)}\quad  We try to input the task instruction and the base recipe into GPT-2: \emph{Please rewrite the recipe for [Target Dish] based on the recipe for [Base Dish]. [Base Recipe]. [Target Dish] is made as follows.} This setting tests whether the model can understand the instruction and compose its knowledge of the target dish into the base recipe.

\textbf{\textsc{EduCat}} is an editing-based model. It starts from the base recipe and edits step by step to meet the target dish. In each step, \textsc{EduCat} detects possible positions in the current generated recipe contradictory to the target dish, proposes a word-level modification operation (\textit{replace/insert/delete} at the position), and decides whether to accept the modification according to its fluency and coherence scores.

\textbf{\textsc{Delorean}} is a constraint-based model. It generates based on the target dish first and iteratively refines the generated recipe to meet the two constraints: the recipe should be coherent with the target dish while remaining similar to the base recipe. \textsc{Delorean} measures the similarity with the KL divergence between the generated recipe and the base recipe.

We equip \textsc{Delorean} and \textsc{EduCat} with the finetuned GPT-2, and use their original hyperparameters.

\section{Related Work}
\subsection{Recipe Processing}
Recipes are a common type of procedural texts, describing the actions a chef needs to perform to cook a specific dish.
Recipe comprehension tasks include entity state tracking~\citep{bosselut2018simulating}, recipe structure extraction~\citep{kiddon2015mise, donatelli2021aligning}, and anaphora resolution~\citep{fang2022does}. These tasks involve the understanding of the relationship between ingredients and actions and test models' abilities under fine-grained supervision. In contrast, we evaluate whether models can understand the recipes and compose them unsupervisedly.
Recipe generation tasks ask models to create recipes from a given title. \citet{kiddon2016globally, h2020recipegpt} provide an ingredient list, \citet{majumder2019generating} add user's historical preference into consideration, and \citet{sakib2021evaluating} generate recipes from an action graph.
\citet{li2021share} introduce the recipe editing task, which expects models to edit a base recipe to meet dietary constraints; and \citet{antognini2022assistive} iteratively rewrite recipes to satisfy users' feedback. Our task is similar to the recipe editing tasks in including a base recipe in the input, but we go beyond simple ingredient substitution in recipes, and also evaluate whether the actions associated with the ingredients are alternated by models.

\subsection{Compositional Generalization}
Compositional generalization is the ability to understand and produce novel combinations of previously seen components and constructions~\citep{chomsky1956syntactic}.
To measure the compositional generalization ability of models, a line of research~\citep{lake2018generalization, ruis2020benchmark} designs tasks that map sentences into action sequences, and splits the data into training and testing sets from different data distributions, e.g., according to different lengths or primitive commands.
Motivated by these works, we divide our data into finetuning corpus and test set based on flavors and ingredients, which are basic components of a dish.
Other compositional generalization works conduct experiments on semantic parsing~\citep{keysers2019measuring,kim2020cogs} and language grounding~\citep{johnson2017clevr}. To the best of our knowledge, our task is the first compositional generalization task in the form of natural language generation.

Our task mainly differs from previous ones in measuring compositional generalization in a realistic setting, where all texts are natural rather than synthetic. The variation in natural language, both the variation of expressions and the variation of knowledge provided by different users, brings extra challenges. \citet{shaw2021compositional} also address the challenge of natural language variation in compositional generalization, but they experiment on semantic parsing, where the knowledge is highly consistent and can be inducted with grammar rules.

\section{Task Definition}
We formulate our task in the form of counterfactual generation~\citep{qin2019counterfactual}:
\begin{equation}
    p(\mathbf{y}'|\mathbf{y}, \mathbf{x}_{\mathbf{y}}, \mathbf{x}'),
\end{equation}
where $\mathbf{y}$ in the base recipe, $\mathbf{x}_{\mathbf{y}}$ is the ingredient set of $\mathbf{y}$, $\mathbf{x}'$ is the adjusted ingredient set that replaces or adds one main ingredient, and $\mathbf{y}'$ is the target recipe to generate.
In Figure~\ref{fig-task}'s example, $\mathbf{y}$ is the base recipe of \emph{red-braised pork}, and $\mathbf{x}'$ differs from $\mathbf{x}_{\mathbf{y}}$ in changing \emph{pork} to \emph{crucian carp}.

redpajama_set_name: RedPajamaArXiv


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Home › Vitraux de la Cathedrale
Vitraux de la Cathedrale, 1957
By Jacques Villon
Lithograph • Signed in plate • 99cm x 62.5cm
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Jacques Villon
French , 1875 - 1963
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Jacques Villon, pseudonym of Gaston Émile Duchamp (born July 31, 1875, Damville, Normandy, France — died June 9, 1963, Puteaux, near Paris). He was a French painter and printmaker who was involved in the Cubist movement; later he worked in realistic and abstract styles.
Villon was the brother of the artists Suzanne Duchamp, Raymond Duchamp-Villon, and Marcel Duchamp. In 1894 he went to Paris to study law, but, once there, he became more interested in art, and he spent the next 12 years contributing illustrations and cartoons to newspapers. In 1903 Villon was one of the founders of the Salon d'Automne, an exhibiting association that was created as an alternative to the traditional Salon. He began to study painting in 1904. In 1906 he moved to the Parisian suburb of Puteaux, where he was able to devote himself primarily to painting.
Villon adopted a Neo-Impressionist style in his first paintings. About 1910, however, he began to develop his mature style, in which he combined a Cubist use of flat, geometric shapes with a palette of luminous colours. He and other Cubist-influenced artists (including his two brothers) formed a group called the Section d'Or (Golden Section) in 1912; Villon suggested the name to emphasize the group's interest in geometric proportions.
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text:
Antal Amade de Várkony was a Hungarian count and notable comes of Zagreb, Croatia, who established the first public theatre in the city of Zagreb in 1797. His father was Count Tádé Amade, (1724-1807), and his mother was the countess Mária Angélika Nyáry von Bedegh (1734-?).

The theatre was called Amadeo's theatre and was open until 1834. Amadeo's theatre was situated in the former Blatna (Mud) and Kazalisna (Theatre) Street, which afterwards got the name Demetrova. After closing his theatre, Amade withdrew to his estate in Hungary. The building in which it was situated is now the Croatian Natural History Museum and, from 2000, the home of the Amadeo Theatre and Music Company.

References

External links
 History of the Croatian National Theatre
 Croatian Natural History Museum
 Amadeo Theatre and Music Company

1760 births
1835 deaths
Theatres in Croatia
Culture in Zagreb
Hungarian nobility
18th-century Hungarian male writers
Masters of the doorkeepers

redpajama_set_name: RedPajamaWikipedia


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text:
Gossip Girl's Season Finale Style
By Joyann King Updated May 14, 2010 @ 3:33 pm
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On Monday night's season finale, Serena, in a Yigal Azrouel dress, Carla Reiter necklace and MCL cuff, and Blair, in a Parker blouse, Topshop skirt and Noir bracelet, turn to each other for advice on their respective dramas. While Blair decides whether to salvage her relationship with Chuck and meet him on top of the Empire State Building, Serena faces a Gossip Girl blast about Dan that threatens her relationship with Nate. Plus, the infamous Georgina Sparks returns and she, as always, has a quite the agenda! Click on to get more dish about Monday's episode (airing May 16th at 8 p.m. EST on the CW) and to find out exactly what the girls are wearing—head-to-toe!


Denver's Country Club neighborhood was developed in the early 1900's and is punctuated by wide parkways and distinctive gates. Located on the National Registry of Historic Places as a designated landmark district, this neighborhood boasts elegant homes and stately mansions designed by famed architects Fisher and Fisher, Jacques Benedict, and Burnham Hoyt, to name a few. Recognized for the exclusive and private Denver Country Club on 1st and Gilpin Streets, this neighborhood boasts 100-year-old trees, easy access to downtown Denver, and a diverse architecture spanning from Tudors and Georgians, to Mediterranean influenced Spanish Colonials. Home prices range from one million to multi-millions, and they don't come on the market often.


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Review: 'White House Down' a silly throwback
'White House Down
Rated PG-13: 137 minutes
Staggeringly implausible, cartoonishly comical, Roland Emmerich's "White House Down" is refreshingly dumb.
Refreshing because carefree action absurdity, once the province of the summer cinema, is on the outs. Solemnity - even for caped, flying men in tight-fitting trousers - is in.
But there's an inarguable, senseless pleasure in watching Jamie Foxx, as the president of the United States, kicking a terrorist and shouting: "Get your hands off my Jordans!" Hail to the chief, indeed.
"White House Down" follows Antoine Fuqua's "Olympus Has Fallen," released in March, as the second movie this year to imagine an assault on 1600 Pennsylvania Avenue. The two films are very similarly plotted, but "White House Down" is notably less serious, more content to loosen the strings and acknowledge its own inherent preposterousness.
This becomes particularly crystalized somewhere around the time Foxx's President James Sawyer and his rescuer, Channing Tatum's wannabe Secret Service agent, are careening across the White House lawn in the president's limo while terrorists shoot in pursuit. Onlookers behind a fence - media, regular people, the Army - merely gape in awe, as if frozen by the idiocy.
"White House Down" is most entertaining when it's a simple, ludicrous buddy movie, with Tatum and Foxx fleeing across the White House grounds, dropping one-liners as they go, eluding a gang of assailants led by a bitter turncoat (James Woods) and his ferocious henchmen (including Jason Clarke, swapping sides in the war on terror following "Zero Dark Thirty").
This is a kind of coronation for Tatum as a movie star. He's now reached the level that he can breeze through a blatantly silly movie and look none the worse for it. He's John Cale (not to be confused with the Velvet Underground musician, although, how could you?), a Silver Star veteran of Afghanistan and a police bodyguard to the speaker of the house (Richard Jenkins).
For his Secret Service interview at the White House, he's brought along his politics-obsessed 11-year-old daughter (the promising Joey King). But it goes poorly, partly because his would-be boss turns out to be an old flame (Maggie Gyllenhaal) who doubts he's grown up. There's some reason to believe her, since Cale (in the mold of most action heroes) is an absentee, divorced dad.
It's an archetype defined by Bruce Willis in "Die Hard," a movie "White House Down" apes right down to the wife-beater tank top. When the Capitol dome is detonated and the White House invaded, Cale is separated from his daughter and stumbles into the kidnapping of the president. From there, it's a series of chases through the handsome, recreated halls of the White House, where golden light filters in through venetian blinds but seemingly scant security measures exist.
Emmerich, the director of spectacles like "Independence Day" (a movie he references in "White House Down") and "2012," has made blowing up the White House something of a fetish, having already done it in both of those movies. It's a style of blockbuster that now feels dated, like a '90s kind of big-budget moviemaking that depends on explosions, flashes of comedy and star charisma.
The charm of Tatum - toned but goofy - carries the film. Foxx, a more gifted comic actor, is left off-screen for large chunks. His president is a kind of liberal fantasy version of Barrack Obama, boldly removing all troops from the Middle East, thereby sparking the fury of the Beltway's white power players.
If "White House Down" had pushed the farce further, Emmerich's overlong romp could have been something special. But the comedy in James Vanderbilt's screenplay only comes in spurts.
Many of its biggest laughs don't come when they're cued up, but at the film's attempts at emotion. Woods, for example, gravely announces: "Killing Ted Hope was the second hardest thing I've had to do in my life." If stripped of its production value, "White House Down" would make one hysterical off-Broadway one-act. (Jake Coyle/The Associated Press)


The transfer of an undertaking from one employer to another is not a rare occurrence in a recession. Greater competitiveness in the private sector leads to situations where enterprises are seeking to consolidate resources in tight markets. Weak companies are also bought by stronger companies in the same field. Neither is the public sector immune from this phenomenon and it is now probable that education bodies, the HSE and local authorities will increasingly seek to outsource services which were once operated in-house. Indeed, there is now also a high incidence of transfer between state bodies as public spending cuts are implemented.
The resulting mergers and takeovers create an enormous sense of insecurity for employees. A European Directive resulted in the European Communities (Protection of Employees on Transfer of Undertakings Regulations) 2003. This is a complex area of law and it is always advised to seek Trade Union assistance when faced with such situations. This month's "Know Your Rights" will look at the necessary basic components for the application of the Regulations. We will examine the situations where the regulations apply. We will consider also the protections afforded to employees as well as the information and consultations requirements. Finally we'll look at the enforcement procedures inherent in the Regulations.
What are the necessary components for the Regulations to apply?
The Regulations do not apply in a situation where another company buys the majority of shares (share sale).
It can apply to a management buy-out and outsourcing, for example.
A legal transfer can include the assignment or forfeiture of a lease or a merger.
Could you be more specific on the transfer from one employer to another?
Yes. The transfer must be an economic entity which retains its identity and there must be a change of employer. In a simple example, a chocolate factory must retain its identity as a chocolate factory with the new employer. If the factory, for example, was immediately converted to a furniture factory by the new employer, then the regulations would not apply.
I work for a charitable organisation which has been taken over by another charity. Neither organisation operates for gain. Am I covered under the Regulations?
Yes. Operating for economic gain is not a necessity and it can therefore include charities, local authorities, health boards, schools and colleges, state and semi-state bodies etc.
What protections do employees get?
Are my pension rights protected?
No. This is one of the weaknesses of the legislation. However, the new employer is obliged to protect and preserve the pension fund which was already in existence.
Can I be dismissed because of a Transfer?
An employee cannot be dismissed for grounds related to the transfer however an employer may use the ETO (Economic, Technical and Organisational reasons) defence to restructure an organisation after the transfer. This could an include an attempt to introduce redundancies.
Surely that is a major weakness from a worker's point of view?
Agreed. There is no timeline specified as to when an employer can attempt to change conditions or introduce redundancies based on the ETO defence. However, third party bodies like the Rights Commissioner service and the EAT would demand a considerable amount of economic evidence from an employer if such a defence is invoked, especially in situations where such a restructuring is attempted within a relatively short time period after the transfer. The role of the Trade Union is crucial in such situations if such attempts by employers are to be successfully re-butted.
What are the obligations to consult?
The "transferor" (original employer) and "the transferee" (new employer) must give information to employees with regard to the date, reasons and the probable legal and social effects of the transfer on the employees. The employer must give this information and consult also with the trade union or representatives of the employees, in the absence of a trade union, not later than 30 days before the transfer is carried out, and in any event in good time before the transfer is carried out. This consultation must be carried out "with a view to seeking agreement".
Where are complaints processed if I believe my rights are being denied?
Complaints must be made within 6 months to a Rights Commissioner. The Rights Commissioner can uphold the complaint and make the employer comply with a certain course of action. The Rights Commissioner can also award compensation of a maximum of up to 4 weeks' pay for breach of the consultation and information requirements and up to 2 years pay for any other breach.
Appeal is to the Employment Appeals Tribunal within six weeks.


Discussion in '3rd Gen. Tacomas (2016+)' started by tcBob, Apr 24, 2016.
OV Tuned, S&B Intake, MBRP Dual black Cat-back, accumulator delete, URD SS, Custom Car Grille, MESO Ultimate Turn Signal, RIGID fogs, BHLM with LED upgrade, Geko Bed & Underhood lighting, Rear camera anytime, Nav Block Override, DE-badged, TVD blackout kit, Sienna mirror control swap, HiLux interior door handles, Hybrid SS/Delrin Shift Knob, Cali-Raised bed Stiffeners, Ultimate Tailgate Seal.
Has anyone tried out the LED bulbs for headlights and foglights on an 18?
I used Auxbeams for both high and low beam. They work great. I completely replaced the OEM fogs with a Rigid fog light kit.
Thinking of buying a third gen. How often should I rotate the air in my tires?
s.e.charles, Paul631, CygnusX191 and 2 others like this.
I just received this a week ago and love it. The tray liners are rather tacky and hold items from sliding really well and holds your change!
Got this in my truck and love it, but just got these to put on the center part where i grab to remove it, I think it will make this much more functional.
I have to get below the tray more than I realized, and it can be awkward to grab.
tostada We are, killing ourselves over ancient literature.
fumoto part number(s) for oil pan and filter? anyone done this?
Any suggestions on lower bumper LED light bars, I was looking at the Cali Raised and Rough Country kits their about the same price any input?
I plan on getting this.
doublethebass and Norsemanvike like this.
I installed the Cali Raised 32' Combo Lower Bumper light and brackets. Pretty straightforward install and everything appears to be high quality. Instruction say to delete air dams, I modified by cutting a couple of inches off the bottom of each (plastic so very simple) and reinstalling.
Watch the Cali raised video and it is really that simple.
3 in leveling, east coast gear supplies bushing, backflip mx4.
Paul631, NunyaBizness, Mac23 and 2 others like this.
Mine arrived Tuesday afternoon. I absolutely love it. Well worth the $9. I don't have to reach to bottom of the endless pit that is my center console.
I have one which is used to keep "a lid" on the storage unit.
Q: how do you get it out to access underneath?
if it's got stuff in it, removing it offers a great opportunity for tip over.
This should have been included with the vehicle. All of my previous cars have had some sort of tray in the center console from the factory. Needless to say, I ordered this before even leaving the dealership with my Tacoma.
I have this too, the center console lift out tray, but its hard to grab on to to lift out, has anyone come up with a handle mod for this to make it easier to lift out? right now, I pull up on a corner to get it out of there....I'm sure there is an easier way.
I cut a slot in the dividing rib and looped a bicycle brake straddle cable up through it. I filed the ends of the cable thin & wedged shape so they would hold themselves in place.
it's still a pain in the differential to lift out.
piece of gorilla tape. skate-tape would probably work great as well, just have to be careful about it scratching things in the tray. sunglasses, etc.


Track every element of a transcation. Spout is more than just a way for you to pull in transaction information from banks and credit cards. From individual charges to vendors, categories and even account information, we've got you covered and we're here to make your life easier.


text:
"B&W IMMERSION: PORTFOLIO OF MY ARTWORKS"...
"BETWEEN LIGHTS AND COLORS: PORTFOLIO OF MY ARTWORKS"…
""Between lights and colors"... Several of my series Painting Drawing Photography Mixed-media Colors ones Like "Fairy scene in the woods"...."Dreamlike seabed"...."Snow time"..."The secret life of mushrooms" and so on... All visible on my website www.al-artwork.com Several of these works won an Award in Usa and Canada the last weeks in 2022 and 2023 with exhibitions won too... They are also in exhibitions In Europe like in Greece with Alesarte Art Gallery Music CloneMeTwice-Inception France 2023 @Anne-Sophie Le Penru My website www.al-artwork.comBetween lights and colors"... Several of my series Painting Drawing Photography Mixed-media Colors ones Like "Fairy scene in the woods"...."Dreamlike seabed"...."Snow time"..."The secret life of mushrooms" and so on... All visible on my website www.al-artwork.com Several of these works won an Award in Usa and Canada the last weeks in 2022 and 2023 with exhibitions won too... They are also in exhibitions In Europe like in Greece with Alesarte Art Gallery Music CloneMeTwice-Inception France 2023 @Anne-Sophie Le Penru My website www.al-artwork.com


text:
Sainte-Marie-de-Chignac è un comune francese di 599 abitanti situato nel dipartimento della Dordogna nella regione della Nuova Aquitania.

Società

Evoluzione demografica

Note

Altri progetti

Sainte--Marie-de-Chignac


Hope Shores Alliance - As northeast Michigan's leading experts on sexual and domestic violence, Hope Shores Alliance believes that everyone has the right to live in a safe environment. Their professional staff supports and advocates for survivors and their families with a variety of free, confidential and voluntary services and resources that include a 24-hour help and support line, advocacy, counseling, supportive listening, emergency shelter, and long term housing. Hope Shores Alliance serves Alcona, Alpena, Iosco, Montmorency and Presque Isle counties.
In addition to the Inclement Weather Procedures found in the Student handbook, there are procedures that apply to employees.


text:
Oxford University Press to showcase its range of Higher Education and English language titles at the Bangalore Book Festival 2018
AcademicsCompetitionEducational News
Bangalore: Oxford University Press (OUP), the world's largest university press, is participating in the Bangalore Book Festival to be held from October 15 to 21, at Palace Grounds in Bangalore. Displaying an array of their new and bestselling titles in the higher education and global academic space, along with digital and innovative learning solutions, OUP will be available to visit at stalls H5 and H6.
Visitors to the OUP stall can avail a chance to check out the myriad collection of books and browse through a variety of subject and domain-oriented titles, in addition to the latest bilingual dictionaries, adult English language teaching and learning material, and textbooks covering areas such as Engineering, Hospitality, Management, and Science & Commerce.
On display will be IELTS General and IELTS Academic, the two latest releases in the Adult English Language Learning space, especially curated to meet the needs of learners preparing to take the IELTS (International English Language Testing System) General/Academic test. With a focus on content based on sections/tasks in the actual IELTS Academic and General tests, the books will help learners to approach topics from the point of view of the test.
Another highlight is the latest addition to OUP's Bilingual Dictionaries Programme – the English-English-Kannada Dictionary – all set to be released this November. With over 52,000 up-to-date entries and related phrases and idioms, the new EEK dictionary is an authoritative dictionary for students, teachers, translators, and general readers, first of its kind, with detailed translations of English definitions in Kannada, and usages in English.
As part of outreach for Higher Education titles, in focus this year will be Engineering, Management, and Hospitality titles, along with the latest releases catering to the AICTE Model Curriculum 2018, for first year B. Tech students. OUP is one of the first few publishers to have released the titles adhering to the new curriculum. The new titles launched by the Press are; Principles of Basic Electrical Engineering, English Language and Communication Skills for Engineers, Programming in C, and Programming in C.
VTU recommended texts on display: Communication Skills as per VTU syllabus 2018, Communication Skills: A Workbook, Programming in C, Protection and Switchgear, Everyday Cryptography, Digital Image Processing, etc. Other new releases on display: Cyber Forensics, PlaceMentor: Tests of Aptitude for Placement Readiness, and Cloud Computing.
Bestselling titles on display: Hotel Front Office, Food and Beverage Management, Food Production Operations, Hotel Housekeeping, Python Programming, Microelectronic Circuits, Digital Signal Processing, Business Ethics, Business Communication, Internet Marketing, and many more.
Himachal Pradesh CM releases book of Dr. Pramod Sharma
IIM Kozhikode completes 100% summer placements in 3.5 days


Lightweight, tough and stylish - the Juno 79cm by Antler is superb.
If what you need from your luggage is an affordable, lightweight and durable case from a brand that you can trust, then the Juno 79cm spinner from Antler is for you. Made from durable Polypropylene that is as lightweight as it is tough, this is a case that will last the distance. Coming in at 79cm means there's plenty of space to carry everything you need. The built in TSA combination lock allows for easier travel, and the 360 degree spinner wheels give you ease of movement. All of these features to go with the smart-looking exterior - the Juno 79cm by Antler is an all around winner.


A desktop is a massive investment decision. It is not a cheap selection, so you want to get the best bang for your buck. If you purchase one that is not right, you will not be able to do every little thing that you want it for. Will not let this take place to you, read through the report beneath.
Usually have anti virus on your desktop. You could get a virus if you will not have this application. You do not require to purchase a desktop with destructive application that also tends to make the computer run slowly and gradually. Numerous plans on the marketplace will run a scan and mend on a timetable if you set it to do as such.
Is your pc sluggish? This is a quick resolve! Operate "ms config" from your start off menu. Listed here you can effortlessly find out how many applications start managing at boot up. If there are some listed you do not use often, disable them. This will expedite the pace of your working program.
Cautiously pick the items you use when creating a desktop personal computer. Some motherboards are incompatible with particular brands of processor. RAM is not going to perform with all motherboards possibly. You should validate compatibility throughout all of the components you acquire. This can save you income and time, and possibly even some head aches.
Very carefully evaluate the place you will be putting your desktop. Each and every model and manufacturer is sized differently. Some have smaller sized profiles, the place others could just take up a lot more vertical room. Make positive that you get a laptop that fits your place.
When you comprehend what you want to get, it need to be simple to uncover the right device. If you have recognized almost everything this article is attempting to educate you, you can be ready to commence buying quickly. Use all of this details to get the most out of your buying experience.
This entry was posted in Computer Cases And Bags on April 26, 2016 by .


text:
Police Commission Rejects Buhari's Former CSO's Service Extension, Orders Refund Of Salaries
15 Persons Abducted As Gunmen Attacks Niger Community
Bola Tinubu Reveals Why He Will Emerge Victorious In 2023 Presidential Election
Actor Frederick Leonard Says 'No Brainwashed Person Should Preach PVC To Him'
Obeying IPOB's Sit-At-Home Order Attracts Severe Punishment In Ebonyi – Umahi
Everton Sacks Rafael Benitez As Manager After Six Months In Charge
The Police Service Commission has rejected the service extension granted to Abdulkarim Dauda, a former Chief Security Officer to President Muhammadu Buhari, by the immediate past Inspector-General of Police, Mohammed Adamu.
Dauda, a Commissioner of Police who was due to retire in January 2020, had his service extended till January 2023, by the former IGP.
The decision to reject the former Presidential CSO' service extension was reached at their 13th Plenary Meeting of the PSC.
The commission which faulted the former IGP's move, also explained that Dauda wrote the PSC seeking promotion to the rank of Assistant Inspector-General of Police (AIG). The former Presidential CSO has also been asked to refund the salaries he earned during the extension period.
Commissioner representing the Media and the South South Zone in the commission, Austin Braimo who addressed journalists at the end of the meeting in Abuja, also announced the promotion of two Assistant Inspectors-General of Police to the rank of Deputy Inspector-General of Police (DIG).
Six Commissioners of Police were also promoted to the rank of Assistant Inspectors-General of Police (AIGs).
Braimo said;
"We want to ensure that justice is done. That is what the board has done. We took the decision so that it will act as a deterrent. The board has taken a decision and the decision will be communicated to the IG."
He further revealed that 17 Deputy Commissioners of Police (DCPs) were promoted to the rank of Commissioner of Police, while 24 Assistant Commissioners of Police (ACP) were promoted to the rank of DCP.
49 Chief Superintendents of Police (CSPs) were elevated to the rank of ACP, while 74 Superintendents of Police (SP) were moved up to the rank of CPS.
866 Deputy Superintendents of Police (DSP) were promoted to the rank of Superintendents of Police (SP), while 716 Assistant Superintendents of Police (ASP) 2 were confirmed ASP 1.
INEC Chairman insists on using technology for elections
BBNaija's JMK Receives N1M From Fans As Birthday Gift, Gets A Shop For Her Business (Videos)
No less than 15 persons have been kidnapped by some gunmen who attacked Kulho village of Mashegu Local Government Area…
The National Leader of the All Progressives Congress (APC), Bola Tinubu has revealed why he would succeed and emerge victorious in…
Nollywood actor, Frederick Leonard says no Nigerian should admonish him about getting his permanent voters cards (PVCs) ahead of the…
No less than 15 persons have been kidnapped by some gunmen who attacked Kulho village of Mashegu Local Government…
The National Leader of the All Progressives Congress (APC), Bola Tinubu has revealed why he would succeed and emerge victorious…
Nollywood actor, Frederick Leonard says no Nigerian should admonish him about getting his permanent voters cards (PVCs) ahead of…
Ebonyi State Governor, David Umahi has warned residents of the state to disregard the recent sit-at-home order imposed by…
Everton Football Club has sacked its manager, Rafael Benitez, after just six-and-a-half months in charge of the premier league…
Peter Obi Replies Man Threatening To Sue Him If He Fails To Declare 2023 Presidency
Former Anambra State Governor and vice-presidential candidate of the Peoples Democratic Party (PDP) in the 2019 election, Peter Obi…
Jega asks National Assembly to drop direct primary clause
A former Chairman of the Independent National Electoral Commission (INEC) , Prof Attahiru Jega, yesterday enjoined the National Assembly…
Soot: Police, NSCDC probe officers over illegal oil refining
The allegations by the Rivers State Governor, Nyesom Wike, that some security officers own illegal refineries have jolted the…
PDP governors meet today in Port Harcourt, 2023 tops agenda
Peoples Democratic Party Governors Forum will hold a special meeting at the Rivers State Government House, Port Harcourt today,…
NDLEA intercepts 1.5m Tramadol tablets going to Kebbi, Kano, other drugs at Lagos…
<!-- NDLEA intercepts 1.5m Tramadol tablets going to Kebbi, Kano, other drugs at Lagos airport, seaport - Champion Newspapers…
Masked men shoot at SDP Candidate in Ondo
Just Like Regina Daniels, I'll Marry A Rich Man Regardless Of His Age – Actress Sylvia Ukaatu
Lawan and his 'bow and go' crew by Oke Umurhohwo
Army ready for Crocodile Smile IV in Lagos, Ogun
If I Had My Way, I Would Kill All My Neighbours – 27-year-old Murder Suspect Confesses


for the past few weeks i have been facing this problem.windows gets automatically rebooted after working a while.this is more frustrating when I am connected to net and in the process of downloading some article.
I use dialup networking to connectto my internet with a 56k modem.
This may be a browser error that could need updating the java script or browser.
Other causes could be the anti-virus running in the background and scanning files while you are surfing the net,check configuration settings.
The other areas to chek is to see if you have bad memory or the CPU may be heating up,another thing that will cause reboots is the power supply.
It most likely has something to due with heat. It most likely isn't any software that would be causing a problem like that. I would check out your CPU fan first and then go from there.
i suspect either heat or your power supply. try running without your computer cover in place. if this solves your problem, then it's heat.
if your bios has hardware monitoring, enable an audible alarm.
If the system reboots while online it could be a problem with your sytem being infected with a trojan. I would recommend going to zdnet.com or cnet.com and get a program like cleaner to check your system to see if it is infected. I had that type of problem as well and it turned out to be a trojan. Trojans usually start upon startup and remain hidden, with these programs another user can execute various commands such as REBOOTING the PC.....some programs are designed to do certain commands randomly such as reboot while offline and such to give the illusion of a hardware issue. Just check it out, there are lots of great anti-trojan programs out there and maybe you already have this type of protection, but I just wanted to make this possibility evident to you, good luck.
your power supply is definitley bad when its getting so hot it just shuts itself down to sort of take a break I guess you could say.


You know when you are on Pinterest and looking at wedding inspo and you see something you really like and want to know more or want to know where you can get it but there's no info?? SO FRUSTRATING I KNOW. So I am here to help! I really want to share a handful of pictures and provide some background behind some of the fun details from our wedding day! We had a few quirky things we added to make our day special and The Grovers took sooo many gorgeous detail shots that I just have to have another excuse to post some more!!
So hopefully there is just one person out there who will read this and get some *inspo* for their own wedding day!
I am so happy that we got these adorable vow books to use during the ceremony. It looked way better than holding a piece of paper!
Along with the *fancy* gifts I got my parents, I got each of them a personalized handkerchief (ft. her original wedding ring that she gifted me that morning) that said something meaningful from my childhood. Surprisingly, these were the gifts that made them cry the most!
Once again, Kaylyn is a star and she made a custom wax seal for our invites and menus and she hand watercolored a map of Pelican Hill and the surrounding area that we sent out with the invites!
Ugh take me back. I love that we chose mirrored vases for the aisle flowers so they look like they're floating. I didn't do any petals on the aisle itself because the train on my dress would've swept them all up like a vacuum.
We went back and forth about how to do the seating chart and I'm so glad we landed on this idea. Also glad that we arranged it by table number instead of name. People are adults, they can find their name! Also make sure it's high enough so people aren't squatting on the ground trying to read their name!
DO THIS DO THIS DO THIS!! Trust me, you do not want people holding their iPhones or iPads up in the air or in the aisle during the ceremony. Make sure your officiant mentions it right before the ceremony starts as well because some people are just too excited and neglect to read your nicely made signs before being ushered to their seats!! TRUST ME.
If flowers are your thing, go big!
So. I was against sand ceremonies because they're usually neon colored and that just wasn't the aesthetic (have I said aesthetic a lot?) I was going for but we wanted to do a unity ceremony besides communion to really symbolize two becoming one and candles weren't gonna work because we were on a bluff over the ocean. SOOOO I actually love what we did. I got sand from my backyard on the Puget Sound in WA and Alec got sand from his backyard in Huntington Beach and we combined them. We also collected a bag of sand from the beach in Australia that we added to our vase when we got home! ALSO we got the gorgeous vintage communion goblet from Etsy! But we forgot to clean it out so it was full of dust when we poured the wine in, lol.
We didn't decide to #hashtag our wedding until a couple weeks before but I'm glad we did because actually a ton of people posted with it! Just make sure it's simple! Also, GET A SNAPCHAT FILTER! They are so reasonably priced and your guests will love it since everyone is on their phones capturing the partayyy!
During cocktail hour, we had these cards out on the tables that said things like "What should we name our kids" and "What's the secret to happily ever after" and "What sights should we see around the world together". Just make sure someone is designated to collect them! We got some really funny responses and now we are naming our firstborn Chandler Bing.
Love love loved our bar signs! Probably my favorite signs…they just looked so cool and were easy for people to read!
We had an open bar but decided not to serve hard alcohol. As much as Alec and I love a good cocktail, once people start downing tequila, it can get out of hand real fast!
This one is a two-fer. 1) I LOVE that we used our engagement photos as our guestbook! People wrote a ton of nice, fun things in the book, they got to look at some of our gorgeous photos (also by The Grovers) and when we look back on it, we can look at our photos plus our guests! 2) The sign behind the guestbook says "Be Our Guest…Please sign our guestbook" so there is Disney detail #2.
We knew we wanted to display a verse and we both loved this one. We made it a part of our vows and now this sign sits in our home! We had our wedding planner move it around throughout the evening so it kept popping up!
This is one of my favorite shots. Posh Peony killed it with flower chandeliers! It glammed up the room 1000%. Alec and I also specifically didn't place any tables behind us even though there was a lot of space because we didn't want people munchin on their dinner in the background of our photos!
I have a good handful of cousins and we are all really close so I didn't want to split them up! We splurged for this ahhmazing king's table that sat all 20 of mine and Alec's cousins and siblings!
Unfortunately, this is literally like the one detail that we didn't get a professional photo of but I was soooo freaking obsessed with these. We had normal table numbers on each table (as you'll see below) but Kaylyn also made us these precious signs that corresponded with each table 1-16. I highly recommend these!!
The table numbers (made by Kaylyn) didn't have to be huge, people can read, but make sure that the order makes sense so when someone sees table 14, they know that table 15 must be near!
Kaylyn made these amazing marble piece that were sitting on each persons place setting and we marked their meal choice with dots on the bottom of the piece. Like one dot means steak, two dots means fish…you get it. I just wanted them to look as simple and classy as possible so they could take them home!
The funny thing is, Alec and I were so busy that we didn't even get to eat any candy before it was all gone!
The patio was poppin!! Especially when we brought the late night snacks out there. Plus we had both bars outside in order to keep the inside clear for dancing! SO happy we invested in some gorgeous lounge furniture!
I spy a sequin tablecloth….(we used these for our sweetheart table, cake table, and candy table)…dreamy!
I gave the bakers at Pelican Hill a rough idea of what I wanted for the cake and they absolutely exceeded my expectation! The best part is that we had them deliver the top layer to our villa that night and we ate it for breakfast the next morning! I am not about that year old frozen cake life!