text:
\section{Introduction}
\IEEEPARstart{C}{reativity} is said to be the generation of a product or service that is judged to be novel and also to be
appropriate, useful, or valuable by a knowledgeable social group \cite{Sawyer2012}, and
is oft-said to be the pinnacle of intelligence.  Due to greater competitiveness in global markets
for all industries, there is need to make product/service development cycles more efficient.  Indeed
creativity is the basis for ``disruptive innovation and continuous re-invention'' \cite{IBM2010c}.
Given limits on human creativity resources, it is important to develop technologies for
greater creativity, either operating autonomously or in collaboration with people.

Can a computer be creative?  Computational creativity is concerned with
machine systems that produce novel and high-quality artifacts (broadly construed) for
the pleasure and consumption of people.  Such systems could produce jokes, poems, visual art,
architectural blueprints, business processes, fashion ensembles, financial service designs, or any other
such artifact that is popularly viewed by people as creative output.  In this paper, we focus
primarily on culinary recipes, which include both the set and quantities of ingredients to be used
as well as the methods and procedures of preparation.  We also discuss menus, which are sequences of
culinary recipes.

We focus on a specific domain because at least with human creativity, there is substantial evidence that
this cognitive ability is domain-specific \cite{KaufmanB2004}, in the sense a good engineer may not be
a good poet.  Notwithstanding, psychometric testing has indicated some correlations of ability that allows
domains to be grouped into categories: expressive creativity (visual arts, writing, humor); performance
creativity (dance, drama, music); and scientific creativity (invention, science, culinary), with
architecture not related to any of these \cite{CarsonPH2005}.  Most past attempts at computational
creativity have focused on either expressive or performance creativity \cite{CardosoVW2009, Wiggins2006,
Boden2010}, whereas we consider a form of scientific creativity
(but see \cite{SchmidtL2009,KingROYABLMPSSWC2009}).

Our computational creativity system operates in stages that are modeled after stages in human creativity
\cite{Sawyer2012}: find problem, acquire knowledge, gather related information, incubate, generate ideas,
combine ideas, select best ideas, and externalize ideas.  As may be apparent, many of these stages require
the processing of external big data sets, as well as large volumes of intermediate data generated by the
system itself.  The staged approach not only leads to modular system design, but also improves
computer-human interaction when operating semi-autonomously.  Developing big data algorithms and systems
is important, but interaction and presenting results to users in ways that allow them to trust insights
is also important \cite{Fry2011}.  In semi-autonomous mode our system takes a \emph{mixed-initiative approach}
where the human and computer have a creation conversation in which each contributes ideas \cite{SmithWM2011},
rather than the computer acting as a nanny, coach, or pen-pal for the human creator \cite{Lubart2005}.

For culinary creativity, we draw on a \emph{variety} of data sets: large repositories of existing recipes
as inspiration, chemoinformatics data to understand food at the molecular level, and hedonic flavor
psychophysics data to predict which compounds, ingredients, and dishes people will like and dislike.
Since these data sets arise from noisy sensors like gas chromatography and from noisy data preprocessing
steps such as natural language processing, there may be issues of \emph{veracity} that algorithms
must be robust to, cf.~\cite{VarshneyVWM2013}.

These data sets are used to develop generative algorithms that intelligently produce thousands or millions
of new ideas from the recipe design space which, for particular dishes and regional cuisine influences,
has a size in excess of $10^{24}$.   The large \emph{volume} of generated ideas must then be evaluated
to select the best ones.  Evaluative metrics are based on principled models of human perception structured
according to ideas from neurogastronomy and derived from recipe, chemical, and psychophysical data.
The information-theoretic functional \emph{Bayesian surprise} is used to measure attraction of human
attention and novelty.  Since the system is meant to support real-time interaction with human creators and
thereby make the product design cycle faster, the system must operate with the \emph{velocity} of thought.

Building on individual recipe design, complete menus can also be created using ideas from topic modeling.
By using the principle of variety across dishes in a menu, measured using a stochastic distance function,
input parameters for dish design may be generated and selected.

Many previous attempts at computational creativity had not been able to achieve a valid selective step
\cite{Sawyer2012}, as we do here.  A central contribution of this work is in showing how bringing data
sources from the creative domain and from hedonic psychophysics together with big data analytics techniques
can overcome this shortcoming to yield a system that produces novel and high-quality creative artifacts.

It is worth noting that selection based on supervised learning trained on complete artifacts is not
appropriate for creativity, as it is for search, since the entire premise is to create novel artifacts
each time rather than to find existing ones.  Basic models of human perception, applied at the
constituent part level, and techniques for building up predictions of quality and novelty for whole
artifacts is critical to our approach.

The remainder of the paper discusses details of data sets, data engineering, system architecture,
data analytics algorithms, and results.  Although we use culinary recipes as the example domain herein,
the basic concepts are generally applicable to big data approaches to computational creativity in
any domain.

\section{Creativity?}
\label{sec:creative:def}

Creativity seems to be a property of cognitive systems,
but how should it be defined and assessed?  One aspect of cognition is understanding where in a museum a new painting
hangs---reasoning about the world as it is, but another aspect is understanding whether that painting
exhibits creativity---reasoning about things that had never previously been imagined.  Deductive and
inductive reasoning about the world are easily assessed since there is often ground truth, but not so with creativity.

One definitional approach is to list several properties of a creative output, such as being novel,
being useful, rejecting previously held ideas, and providing clarity \cite{NewellSS1963}.  But this definition does
not provide an operational method of assessment.  Viewing creativity as a relationship between the
creator/creation and an observer \cite{Wiggins2006}, if a human evaluator deems something creative, we say
it is creative \cite{PeaseC2011}.  Therefore, by definition, creativity is only meaningful in the presence
of an audience perceiving the creation.  To formalize, we adopt a definition of creativity
used in human creativity research.

\begin{defin}[\cite{Sawyer2012}]
\label{def:saw_create}
Creativity is the generation of a product that is judged to be novel and also to be appropriate, useful,
or valuable by a suitably knowledgeable social group.
\end{defin}

In this definition, there are two dimensions: novelty and quality.  Although there is an individual component
to creativity, the adopted standard is a social one.  An artifact that is novel to the creator
need not be novel to the social group; what the creator finds of high-quality may not be seen as such
by the social group.  Thus creativity is fundamentally socially constructed.  A computational creativity
system has no meaning in a closed universe devoid of people.

The most common way to assess creativity of an artifact under this definition is the Consensual
Assessment Technique (CAT) \cite{GetzelsC1976}, where the creativity of an artifact
is rated by two or more experts in the field. The measured creativity
is the average rating of the judges.  Although it may seem this methodology is too subjective,
many studies have demonstrated that ratings of experts are generally highly correlated, yielding good
interrater reliability \cite{KaufmanBC2009}.  In contrast, novice ratings are not highly correlated
and so novices should not be used for the CAT \cite{KaufmanBCS2008}.

An alternate definition for computational creativity would be by analogy to the Turing test---a system is
creative if it produces artifacts indistinguishable from those produced by humans or having as much
aesthetic value as those produced by humans \cite{Boden2010}.  We do not use this definition.

Our view is that a computational creativity machine without a way to evaluate its potential
outputs is not really a computational creativity machine because generation and assessment must coexist
for proper functioning.  In the same way that information cannot be encoded without a model of the receiver
that will decode that information \cite{Shannon1950b}, artifacts cannot be created without a model of human evaluators.

The lack of evaluative and selective ability has been a primary criticism of many previous computational
creativity systems.  Consider the computational creativity system for visual art
AARON.  AARON generates $150$ pieces a night, but H.~Cohen decides which $5$ to print by viewing
them all: ``AARON doesn't choose its own criteria for what counts as a good painting\ldots
To be considered truly creative, the program would have to develop its
own selection criteria; Cohen was skeptical that this could ever happen'' \cite{Sawyer2012}. A computational
creativity system for mathematical proofs AM suffers in the same way: ``The first and biggest problem
is that AM generates a huge number of ideas, and most of them are boring or worthless; Lenat has to sort
through all of the new ideas and select the ones that are good'' \cite{Sawyer2012}.

Fixing the operational view of Def.~\ref{def:saw_create}, we progress towards
computational creativity system design \cite{Jordanous2012}.  As creativity is only meaningful in the presence of human
perception, a human model is useful for the system to know whether or not it is
producing creative artifacts and to guide its design process.  Such a component cannot be the final
arbiter of creativity as that is a purely human determination, but it can be a very useful aid.
A primary contribution of this work is to develop a data-driven evaluative/selective component.

\section{Stages of Human Creativity}
We use stages of human creativity to guide our computational creativity system design.
This will lead to a modular system architecture.  Note that stages are not always followed sequentially
by human creators; there can be backtracking and jumping around.

When the system operates in semi-autonomous mode, the computer acts as a colleague or partner to the human,
and so following the natural human process improves computer-human interaction.  Indeed
there is an emerging consensus that even in purely human contexts, interacting groups are more creative than
individuals, hence the value of computer-human interaction.

We review stages of creativity delineated by Sawyer \cite{Sawyer2012}, given in Fig.~\ref{fig:stages}
(which also depicts stages where human interaction is possible).

\begin{figure}
  \centering
  \includegraphics[width=3in]{stages.png}
  \caption{Stages in the cognitive process of creativity \cite{Sawyer2012}. In computational creativity, blue/gold stages may have
	significant human-computer interaction whereas blue stages involve autonomous computer operation.}
  \label{fig:stages}
\end{figure}

\begin{enumerate}
  \item \emph{Find the problem}: For ill-defined problems like creating new products, the first step
is to actually identify and formulate the problem using \emph{divergent thinking}.  Exceptional creativity is
more likely when people work in areas where problems are not specified \emph{a priori}.

  \item \emph{Acquire knowledge}: The second stage is to
learn everything there is to know about the problem, especially in terms of past creative artifacts.
Without knowing what has already been done, there is no fodder for
inspiration nor is there a way to judge novelty.  Since it is impossible to be creative without first
internalizing the creative domain, data intake is necessary for creativity.

  \item \emph{Gather related information}: Besides learning about past examples of creative artifacts
within the domain, it is important to absorb information from a wide variety of other sources, so as to
link new information with existing problems and tasks.

  \item \emph{Incubation}: In human creativity, it is important to give the mind the time to process
	all of the gathered information, and to let the subconscious search for
	new and appropriate combinations.

  \item \emph{Generate ideas}: After incubation, the mind is ready to generate ideas.  The generation
of ideas is often considered the key step in creativity and is rather different from other forms of
reasoning such as induction or deduction.

  \item \emph{Combine ideas}: There is often value in cross-fertilization of ideas across problems
and domains. Approaches to combining concepts across domains include attribute inheritance, property
mapping, and concept specialization.

  \item \emph{Select best ideas}: After a new idea or insight emerges, the creator must determine
whether it really is good.  This stage is sometimes referred to as
\emph{convergent thinking}. In two-stage models, convergent thinking follows the divergent thinking
phase of creativity.  The evaluation stage is fully conscious, drawing on large
amounts of domain knowledge to assess novelty and quality.

  \item \emph{Externalize the idea}: Successful creation requires not only ideas but also
execution of those ideas, by identifying necessary resources to make them successful, forming plans
for implementing the ideas, and so on.  This final stage is mostly conscious and directed.
\end{enumerate}

This staged view of creativity forms the starting point for system architecture design.

\section{Creativity System Architecture}
In this section, we propose a system architecture for computational creativity that includes a
data absorption and organization component, as well as a data-driven assessment component that
models human perception.

A block diagram for a proposed computational creativity system is presented in
Fig.~\ref{fig:blockdiagram}, with three main data analytics components: a work planner, a work
product designer, and a work product assessor, which interact to output a work product and work plan.
These components are fed by a domain knowledge database and knowledge categorizer.
\begin{figure}
  \centering
  \includegraphics[width=3.5in]{block_diagram.png}
  \caption{Block diagram of computational creativity system that produces a work product and a work plan.}
  \label{fig:blockdiagram}
\end{figure}
It is important to note that in our proposed system, the work planner and the work product assessor
do not directly interact, but only do so through the work product designer.

The domain knowledge database represents information collected on the creative field of interest,
including information on styles, tastes, constituents, combinations, evolution,
regionality, culture, and methods of preparation.  It also includes a repository of existing
artifacts that have been deemed creative by human audiences.  This knowledge is resolved and
organized by the knowledge categorizer.  It is the source of data that the designer, planner,
and assessor components draw from.  Information from related but distinct fields to the
creative domain are also kept in the database.  As we will see, significant data engineering and natural
language processing is required for creating and using this knowledge database.

The designer generates new ideas for artifacts.  The assessor evaluates those potential design ideas
for creativity and the planner determines the methods by which the ideas could be externalized.  All
three components take input from the categorized database: the designer to draw inspiration for new
ideas, the planner to learn from extant methods of preparation, and the assessor to evaluate a
design idea against the repository of existing artifacts as well as against properties of
constituents and combinations for creativity.

The designer is the lead component of the system.  Although it is possible to use human-like generative
processes, a generation or design procedure wholly different from the human approach is valuable
precisely because it would create things different from what a human would.  It may have
different kinds of `illusions' or `blindspots' than a human, and thus would be a great supplement
or support to human creativity.  These differences enlarge the hypothesis space and allow the machine
to break new creative ground.

A creative computer is limited if it cannot evaluate
proposed artifacts for creativity.  The assessor component models human perception, taste, and culture using data
analytics.  It examines creative ideas produced by the designer along two main dimensions:
novelty and quality.  These metrics are defined on the basis of data sets within the creative domain,
information related to the domain, and experimental data from hedonic psychophysics.
It is worth noting that unlike many other data analytics problems and systems, computational creativity
is fundamentally not a supervised learning problem.  One must decompose artifacts into parts and have
assessment methods for the parts and for the recombination rules, to predict quality of completely new artifacts.
There will not be training data available for novel complete artifacts.

Novelty can be assessed via information-theoretic or other similar quantifications of innovation
within the context of all other existing artifacts in the domain of interest.  Quantifying quality
requires a strong cognitive model because the quality of a creation truly is in the eye (or nose or tongue)
of human beholders.  The novelty dimension is less specific to the particular creative domain of interest,
whereas the quality dimension is intimately tied to it.  We provide details for a specific domain, the
flavor of food, in the next section.

The final component, the work planner, determines steps needed to take concept to externalization.
The work plan provides constraints on what designs are possible and can be optimized for efficient production,
e.g.\ using techniques from planning and operations research.  Generating the plan is itself a creative act
and may be judged as such if an audience observes production.  However, artifacts can be deemed
creative even if the work plan used to produce the artifact is not observed.

\section{Human Flavor Perception}
\label{sec:flavor}

Previous sections have been general; in this section we focus on the culinary domain and
describe current understanding of human flavor perception, following the neurogastronomy
paradigm \cite{Shepherd2006}.

Human flavor perception is very complicated, involving a variety of external sensory stimuli and internal states
\cite{Shepherd2006}.  Not only does it involve the five classical senses, but also sensing through the gut, and the
emotional, memory-related, motivational, and linguistic aspects of food.  First of all there are the basic tastes:
sweet, sour, salty, bitter, and umami.  The smell (both orthonasal and retronasal olfaction) of foods is the key contributor to
flavor perception, which is in turn a property of the chemical compounds contained in the ingredients \cite{Burdock2009}.
Olfactory perception is integrative rather than analytic, yielding unified percepts \cite{SnitzYWFKS2013}.
There are typically tens to hundreds of different flavor compounds per food ingredient \cite{AhnABB2011}.

Other contributors to flavor perception are the temperature, texture, astringency,
and creaminess of the food; the color and shape of food; and the sound that the food makes.  The digestive
system detects autonomic and metabolic properties of food.  Moreover, there are emotion, motivation,
and craving circuits in the brain that influence flavor perception, which are in turn related to language,
feeding, conscious flavor perception, and memory circuits.  Further, stimuli beyond the food itself, such
as ambience of the room, influence flavor.

The complication in flavor perception is due to the interconnection and interplay between a multitude of neural
systems, many of them not memoryless.  Recreating such a flavor perception system in a computer is an ambitious
goal, but any progress is progress towards a viable computational creativity system
for food.  Also, note that simply describing the factors and pathways of flavor perception fails
to consider the settings of those factors that make food flavorful.  We return to this point in
Sec.~\ref{sec:assessment}, where we propose methods for work product assessment motivated by human
flavor perception.

\section{Culinary Recipe Design}
\label{sec:recipe}

In the food domain, a dish is the basic unit of creation.  A cooked and plated dish is presented to a diner
who perceives it and determines whether it is creative.  This presented dish can be the work product produced by
a culinary computational creativity machine, as described generally in Fig.~\ref{fig:blockdiagram}.  The other
output in Fig.~\ref{fig:blockdiagram}, the work plan, is a description of how to cook and how to plate the dish.
A recipe is a work plan for how to cook a dish, but it is also a description of the work product, as it
describes the ingredients to be used, their quantities, and their transformations and combinations.

A menu is a set of dishes that together constitute a meal. For example, a menu may consist of: asparagus soup,
fillet of sole in lemon butter sauce, side of black beans with cilantro, cheesecake with coffee.
A menu is an example of a sequence of artifacts where creativity is important; other examples of artifact sequences
include albums of songs, comedy specials with several jokes, and sequences of clothing ensembles.

Cutting-edge chefs must have impeccable culinary technique, but become renowned for their creative
recipe designs.  A computer system to create novel and flavorful recipes as judged by people, would
certainly be deemed creative.

The computer-generated culinary recipe design problem is not just one of locating existing recipes and recommending
them \cite{TengLA2012}, but of creating new ones.  It is different from web search and product recommendation, and
is truly part of an emerging computing paradigm distinct from fields such as information retrieval and statistical learning.

Culinary computational creativity has recently been discussed in \cite{MorrisBBV2012}, where the authors focus
only on soups rather than general recipes, and do not consider recipe assessment; in particular, they do not consider
any of the neural, sensory, or psychological aspects of flavor.  In our recent previous work
\cite{BhattacharjyaVPC2012}, we discuss general conceptions of novelty and flavor of dishes, but neither
contextualize them nor present an overall system.

The overall culinary recipe design problem has many facets.  Through the lens of Fig.~\ref{fig:blockdiagram}, the
first is to design and construct a suitable domain knowledge database.  This requires a data model enabling the
system to reason about food and support algorithms for design, assessment, and planning.  In particular, it
should be a repository of food ingredients and existing recipes, but also include knowledge about culinary
styles and techniques, regional and seasonal cuisines, flavor compounds and their combinations, etc.
We propose and discuss a data model for food in Section~\ref{sec:datamodel}.

A related aspect to building a computer chef is ingesting and processing raw data to populate the knowledge database
structured according to the data model.  Sources include cookbooks and other repositories of recipes, culinary
guides that explicate the culture of food, repositories of culinary techniques, and chemical databases of
food ingredient constituents.

Given a designed and populated domain knowledge database, a next step is developing a way to generate recipe ideas.
Since cuisine naturally has evolutionary properties \cite{KinouchiDHZR2008}, i.e., cooking styles, techniques,
and ingredient choices evolve and even exhibit features like the founder effect, genetic algorithms are
one approach to the recipe design problem \cite{VeeramachaneniVO2012}.  Such an approach involves mutating
and recombining existing recipes and can produce a myriad of potential recipes.

Besides random mutations and recombinations of recipes, there are some prominent culinary design principles
that can be utilized.  For example, two principles focused on the chemosenses are the flavor pairing
hypothesis \cite{AhnABB2011} and olfactory pleasantness maximization \cite{HaddadMRHS2010}.  Additional
principles center around similarity of ingredients in properties such as geographic origin and seasonal origin.
Chefs may also want to maintain balance (in terms of tastes, temperatures, or textures), or on the
contrary accentuate a given characteristic, e.g.\ create the beefiest burger or the crunchiest cookie.

Finally, as discussed at the beginning of this section, a recipe is not only a work product but also a
rudimentary work plan.  Therefore, in the culinary domain, a plan to produce the artifact is a must.
The plan, utilizing a machine system's strengths, may be optimized and parallelized by formulating
an operations research problem \cite{SwartD1981}.

\section{Data Engineering}

\subsection{Artifact Data Model}
\label{sec:datamodel}
Here we propose a data model that allows us to capture the salient pieces of domain knowledge
to support all of the components of machine-generated creative recipe design.

As discussed in Sec.~\ref{sec:recipe}, the basic unit of cuisine is the dish, which is represented as
a recipe.  We propose a representational model for culinary computational creativity that too has a
recipe as the basic unit.  A schema---a codification of experience that includes a particular organized way
of perceiving and responding to a complex situation---for cuisine that we propose is shown in
Fig.~\ref{fig:recipeschema} and Fig.~\ref{fig:ingredientschema}.
\begin{figure*}
  \centering
  \includegraphics[width=7in]{RecipeSchema.png}
  \caption{Knowledge representation schema for culinary recipes.  The ingredient component is expanded upon in Fig.~\ref{fig:ingredientschema}.}
  \label{fig:recipeschema}
\end{figure*}
\begin{figure*}
  \centering
  \includegraphics[width=7in]{IngredientSchema.png}
  \caption{Knowledge representation schema for culinary ingredients that is a part of the overall schema for recipes given in Fig.~\ref{fig:recipeschema}.}
  \label{fig:ingredientschema}
\end{figure*}

Within this representation, we first capture the basic factors of the recipe, including the ingredients
and their quantities, the tools required, and the sequence of cooking steps with input, output, tool and
duration specified.  These basic factors are enough to be able to produce the artifact, i.e.~the dish.
However, we need more elements in the representation to enable creative, flavorful idea generation by a computer.

We must include knowledge about cultural context, human ratings, chemical analysis of ingredients and
processes, and so on, to be able to characterize and emulate flavor perception.  For example, we include the
name of the dish because it relates to the influence of cortical language circuits on flavor perception.
We include the regional cuisine to which the dish belongs because regionality is a design principle in cooking.
Similarly, we include the chemical flavor compound constituents of ingredients because flavor compound
sharing is another design principle.

As the preceding examples of data model elements illustrate, a creative culinary system's knowledge
representation needs much more than simply a recounting of the ingredient list and cooking steps because
it must reason about flavor perception, which involves many diverse sensing and memory pathways.  Idea
generation can only use attributes in the data model and nothing more.
It truly is the case that how the world is internally represented impacts what can be created.
Creation, in our view, is the process of decomposing artifacts into their constituents as
depicted in the data model, and then recomposing and reconstituting new artifact ideas.

Philosophically, schemata and diagrams define the universe within which cognition takes
place \cite{Netz1999,Kaiser2005}.  Without a selective, simplified universe containing blindspots,
the deployment of reasoning resources becomes untenable.  In the culinary
case, we certainly have blindspots in our proposed schema.  For example, we do not include
a data element about sound even though, as discussed in Sec.~\ref{sec:flavor},
it is a contributor to human flavor perception.  Since sound is not in the schema it is
also outside the universe of reasoning for the system.  Importantly, it is purposefully not in the
schema because capturing every component of flavor would be unmanageable.

\subsection{Natural Language Processing}

Recipes originally written in human readable format must be parsed to extract key knowledge for the data model \cite{TasseS2008}.
Beyond just ingredient lists as in other work \cite{TengLA2012,AhnABB2011}, our system needs
to understand the inputs, outputs, tools, times, and techniques of the recipe steps.

This is performed using natural language processing; our approach for processing ingredient amounts, names, and
processed states is rule-based whereas our approach for processing the recipe instructions is based on statistical
parsing with domain-specific tokens. Crowdsourcing has been used to develop an initial labeled corpus that can
be bootstrapped for improved statistical parsing.  As compared to training on general corpora (Wall Street Journal),
naive statistical parsers trained on both general and domain-specific corpora can have accuracy that improves
from $65$\% to $85$\%, in terms of getting the task, tool, ingredient, and tip correct from a recipe instruction sentence.

Recipes from data sets produced through peer production (as in the 25,000 recipes available on Wikia)
come in various styles and are not as structured as recipes in published cookbooks, presenting extra challenges
we must handle.  Some notable attributes include personal commentary, multilingual text, missing information,
abstracted description, and implied temporal information.

\subsection{Related Information}
Besides ingesting repositories of extant recipes, it is also important to gather related information.  One source
is Wikipedia, as a description of regional cuisines.  Again, natural language processing is needed
to convert text into insight, e.g.\ which ingredients are typical or canonical for a given region.  There
are hundreds of regional cuisines to be understood.

Another source of data, especially important for computational creativity, is hedonic psychophysics data
linked to chemical informatics data.  This provides characterizations of which flavor compounds are present
in which ingredients, and how much people like those flavors according to human psychophysics experiments.
Each ingredient may contain hundreds of flavor compounds in varying concentrations, as determined in
the Volatile Compounds in Food 14.1 database (VCF) and in Fenaroli's Handbook of Flavor Ingredients
as processed and released in \cite{AhnABB2011}.

Since experimental psychophysics data may be sparse with respect to the thousands of
flavor compounds present in foods, data is also needed to predict the hedonic percepts of unmeasured
compounds.  This requires further physicochemical data on the various compounds;
there can be hundreds or thousands of physicochemical descriptors such as the number of atoms
or the molecular complexity.  Much of this data is already structured in databases, but mapping named entities
across databases remains a problem that we solved manually.

\section{Data-Driven Assessment}
\label{sec:assessment}

We now turn to data-driven approaches for assessing novelty and flavor, which draw from human flavor perception
science and operate within the universe set forth by the data model and related data.  We begin with a computational
proposal for novelty, which can be applied more generally to other creative endeavors as well.  We then develop a
computational quantification of pleasantness for food.  A creative recipe should have large values
for novelty and pleasantness quantifications.

\subsection{Novelty}
\label{sec:assessment:novelty}

An artifact that is novel is surprising and changes the observer's world view.  Novelty can be quantified by
considering a prior probability distribution of existing artifacts and the change in that probability distribution
after the new artifact is observed, i.e.~the posterior probability distribution.  At the level of observable
representation of artifacts, the difference between these probability distributions describes exactly how
much the observer's world view has changed.  In recent work, such a quantitation has been given the name
\emph{Bayesian surprise} and has been shown empirically to capture human notions of novelty and saliency
across different modalities and levels of abstraction \cite{IttiB2009,BaldiI2010,Varshney2013}.

Surprise and novelty depend heavily on the observer's existing world view, and thus the same artifact may be
novel to one observer and not novel to another observer.  That is why Bayesian surprise is measured as a change
in the observer's specific prior probability distribution of known artifacts.

Bayesian surprise is defined as follows.  Let $\mathcal{M}$ be the set of artifacts known to the observer,
with each artifact in this repository being $M \in \mathcal{M}$.   Furthermore, a new artifact that is observed
is denoted $A$.  The probability of an existing artifact is denoted $p(M)$, the conditional probability of the
new artifact given the existing artifacts is $p(A|M)$, and via Bayes' theorem the conditional probability of
the existing artifacts given the new artifact is $p(M|A)$.  The Bayesian surprise is defined as the
following Kullback-Leibler divergence:
\begin{align}
\label{eq:Bs}
	\text{Bayesian surprise} &= D(p(M|A)\mid\mid p(M)) \\
		&= \int_{\mathcal{M}} p(M|A) \log\frac{p(M|A)}{p(M)}dM.\nonumber
\end{align}

Thinking of an artifact as an unordered tuple of $N$ ingredients, $A = \{I_1,\ldots,I_N\}$, combinatorial
expressions for probability distributions are found.  Although there are sophisticated techniques for estimating
information-theoretic functionals from data \cite{WangKV2009}, we find plug-in estimators to often be sufficient.
Handling the \emph{unseen elements} problem in statistical estimation \cite{OrlitskySZ2003}, however, is critical
in computational creativity since the goal is to create completely novel artifacts.

\subsection{Flavor Pleasantness}
\label{sec:assessment:pleasantness}

The other dimension of creativity is the pleasantness of the flavors.  As noted in Sec.~\ref{sec:flavor},
knowledge of the senses and perceptual pathways gives insight into factors that determine the flavor of food.
As noted there, constituent flavor compounds sensed by the olfactory system are the key to flavor perception.
Thus a tractable step towards a data-driven model for flavor pleasantness is a model for odor pleasantness.

Recent work has shown that there is a low-dimensional, almost scalar, hedonic quantity that describes the
pleasantness of odors to humans, regardless of culture or other subjectivity \cite{KhanLFALHS2007,HaddadMRHS2010}.
Moreover, this pleasantness is statistically associated with the physicochemical properties of compounds.  Hence
we develop regression models to predict human-rated odor pleasantness of chemical compounds using their properties
such as topological polar surface area, heavy atom count, complexity, rotatable bond count, and hydrogen bond acceptor count.
Starting with tens of physicochemical features for $70$ observations in a pleasantness-labeled training data set \cite{HaddadMRHS2010},
multiple linear regression with model selection based on smallest prediction error in either 10-fold or leave-one-out
cross validation yielded the small set of features used in the final regression model.

The idea is shown in Fig.~\ref{fig:regression}, where data points are individual compounds,
the vertical axis is the human-rated pleasantness, and the horizontal axis is a learned combination of
chemical property features. Given a previously unrated compound, the regression model can be used to predict its pleasantness.
\begin{figure}
  \centering
  \includegraphics[width=3in]{fig2.png}
  \caption{A regression model to predict pleasantness using the physicochemical properties of flavor compounds.}
  \label{fig:regression}
\end{figure}

There is evidence that pleasantness is an approximately linear property of compounds \cite{LapidHS2008}.  If
two compounds are mixed together and smelled, the hypothesis is that the odor pleasantness of the mixture is
approximately a linear combination of the pleasantness values of the individual compounds.  With such linearity,
one can predict the pleasantness of food ingredients that contain several flavor compounds and of dishes that
in turn contain several ingredients.  The chemical properties of flavor compounds are well-catalogued and
there is a growing body of literature cataloguing the flavor compound constituents of food ingredients \cite{AhnABB2011}.

Thus, if the recipe assessor is given a proposed idea by the recipe designer in a computational creativity system,
it can calculate its novelty using Bayesian surprise and calculate its flavorfulness using an olfactory pleasantness
regression model applied to its constituent ingredients and flavor compounds in those ingredients.
Such scoring represents a data-driven approach to assessing artifacts that have been newly created and have never
existed before.

\section{Computer-Human Interaction for Semi-autonomy}
Although the computational creativity system defined thus far can operate autonomously, it can have greater
impact as part of an integrated collaborative work flow with human creators.  We implement an interactive interface,
taking a mixed-initiative approach to human-computer interaction via turns between human and computer \cite{SmithWM2011}.

The first step in creativity is problem-finding.  Mediated by a novel interactive interface design, this may be accomplished
jointly by the human and the machine, by picking a key ingredient, one or more regional cuisines to influence flavor,
and a dish type such as soup or quiche.  Machine learning is used to suggest ingredient types, though this can be modified
by the human.  The problem-finding input screen, Fig.~\ref{fig:problemfinding}, sets parameters for the generative algorithm
to create thousands or millions of ingredient list ideas.

\begin{figure}
  \centering
  \includegraphics[width=3.8in]{problemfinding.png}
  \caption{Interface for problem-finding, showing common and uncommon choices.}
  \label{fig:problemfinding}
\end{figure}

The penultimate stage in creativity is selecting the best idea(s).  The computer predicts which generated recipes
will be the most surprising to a human observer, will be perceived as the most flavorful, and will have the best
pairings of ingredients (see \cite{AhnABB2011}).  These metrics are used to rank the generated ideas and then a
human makes the final selection, see the selection screen: Fig.~\ref{fig:selection}.  In our experience, humans
select one of the top ten ideas, rather than looking through hundreds or thousands of possibilities.
Hence selection is truly a collaboration between human and machine.

Visualizations at the bottom of the screen help the human understand the reasoning used by the computer in generating
and ranking ideas, so as to provide confidence.  This includes visualizing the design process, as well as
the metrics of pleasantness, pairing, and surprise.

\begin{figure}
  \centering
  \includegraphics[width=3.8in]{selection.png}
  \caption{Interface for selection, showing design reasoning and ratings along dimensions of novelty and quality.}
  \label{fig:selection}
\end{figure}

The final stage of creativity is externalizing.  In recipe creation, this involves coming up with not just
the list of ingredients (the focus of idea generation and selection), but also proportions and
recipe steps.  Professional chefs often operate without computer support for externalizing, but amateurs
appreciate guidance since it too requires significant creativity.  The final screen shows proportions
and steps in the form of a directed acyclic graph, Fig.~\ref{fig:steps}.
Possible actions are abstracted to improve reasoning.

\begin{figure}
  \centering
  \includegraphics[width=3.8in]{steps.png}
  \caption{Bottom of interface for externalization, showing recipe steps and their partial ordering.  Green boxes
are recipe ingredients and orange boxes represent actions performed. For the example quiche recipe depicted:
vegetables are cut and fried together, wet ingredients are mixed, pie crust dough is rolled, etc.
Steps can be performed by multiple cooks in parallel, until all elements are put together, baked, and cooled.}
  \label{fig:steps}
\end{figure}

\section{Menus of Recipes}
So far we have discussed creating a single recipe at a time, and in the previous section problem finding was cast
as human-machine interaction for picking a key ingredient, regional cuisines, and dish types.  When creating a sequence
of dishes, such parameters should be linked across dishes.  Here we introduce the notion of dish \emph{variety},
which we consider an important aspect of creative menus.  Taking a big data approach,
we use a modeling technique based on topic modeling. Topic models are used to identify underlying latent topics
in a set of documents; we apply them to a repository of recipes.

\subsection{Topic Modeling}
Def.~\ref{def:saw_create} requires creative artifacts to be novel and of high quality. For menus, the novelty and quality
of the set is partially determined by its constituent dish recipes, but variety is a property of multiple artifacts:
it is an emergent property for collections and is not definable for individual artifacts.

Topic models are machine learning algorithms that discover the main underlying themes that pervade
a large collection of documents through generative model assuming documents are probabilistic mixtures of a set of
underlying latent variables, i.e.\ ``topics'', and the ``words'' that comprise a document are probabilistically
generated from these topics \cite{Blei2012}. Here we treat recipes as documents, and apply the Latent Dirichlet
Allocation (LDA) method of topic modeling \cite{BleiNJ2003} to the Wikia corpus of recipes to indicate how big data
approaches can be used for problem finding.  While applying this method, we assume that a recipe is adequately summarized
as a set of ingredients, but see Sec.~\ref{sec:datamodel}. Topic modeling has previously been applied to recipes
for other purposes \cite{Nedovic2013}.

Learned recipe topics can be interpreted as some ingredient combinations that either go well together (like sauces)
or that can be substituted (like a set of fruits). To generate a new recipe, one selects an ingredient by first choosing
an underlying recipe topic, and then drawing the ingredient from the recipe topic-specific distribution.
Fig.~\ref{fig:topics} shows a list of a select few topics that were generated using the LDA method applied
to the Wikia corpus, along with some of the most likely ingredients in these topics.

\begin{figure}
  \centering
  \includegraphics[width=3.5in]{topics.png}
  \caption{Example topics when the LDA method is applied to Wikia recipes.}
  \label{fig:topics}
\end{figure}

\subsection{Assessing Variety}
Here we propose a topic-based approach for assessing variety in menus. Consider a menu with $K$ recipes, where the $k$th recipe
is $A_k = \{I_{k_1},\ldots,I_{k_N}\}$ and $I_{k_n}$ is the $n$th ingredient in the $k$th recipe.  For notational convenience, assume
all recipes in the menu have the same number of ingredients $N$, but the method applies to the general case where recipes have varying
numbers of ingredients.  Suppose that a topic model with $L$ underlying recipe topics has been used to model the generative process
by which the corpus of recipes was created. Let $T$ denote the random variable for the marginal distribution of the recipe topics
from which an ingredient is selected for a recipe, and let $t_{\ell}$ be the $\ell$th topic.

Note that a topic model is a Bayesian model that considers the relationship between the parameters, the recipe topics and the ingredients
that are chosen in recipes. Therefore, we can use Bayes' rule and perform inference to compute the probability that a particular recipe
topic was selected in picking a particular ingredient. Let $P(T|I_{k_n})$ denote the probability distribution over recipe topics for the
$n$th ingredient in the $k$th recipe. This probability measures how an ingredient is associated with the underlying themes in the recipe
database.

To measure variety in menus, we compare how various recipe topics are spanned by the constituent recipes through the notion of a
\emph{topic spanning metric} for a recipe, which measures how that particular recipe is associated with the various recipe topics.
This topic spanning metric for a recipe can be any function of topic probabilities for its constituent ingredients:
\begin{equation}
s_k = s(A_k) = f\left[P(T|I_{k_1}),\ldots,P(T|I_{k_N}) \right] \mbox{.}
\end{equation}
An example is when a topic is said to be covered by a recipe when at least $1$ ingredient in that recipe was selected from that
topic. Let $s_{k_{\ell}}$ be the probability that the $\ell$th topic was used by at least $1$ ingredient in the $k$th recipe, in which case:
\begin{equation}
s_{k_{\ell}} = 1 - \prod_{n=1}^N \left[1 - P(T = t_{\ell} |I_{k_n}) \right] \mbox{.}
\end{equation}

The following vector is a potential topic spanning metric; it measures the degree to which every recipe topic is associated with the
$k$th recipe: $s_k = \{s_{k_{\ell}}\}_{\ell = 1}^L$. We can now score menu variety based on the distance between spanning metrics for
recipes in the menu. Note that all recipes have vectors of the same dimension, which is the number of recipes topics.
\begin{equation}
\label{eq:var}
\text{Variety} = D[s_1,\ldots,s_K]\mbox{,}
\end{equation}
where $D[\cdot]$ is a distance metric like Euclidean distance.  The variety score computed in this fashion assesses how
recipes in the menu differ from each other in terms of the fundamental underlying themes from which they were generated.
Thus the topic modeling approach allows for the effective use of data to identify and model variety in menus.

\section{Variety, Veracity, Volume, and Velocity}

It has been said that ``Big Data is all about better analytics on a broader spectrum of data'' \cite{ZikopoulosDPDCG2013}
so as to provide insight and value.  In particular, the four ``V''s of Big Data: variety, veracity, volume, and velocity
must all be considered to derive maximal value in data-driven approaches that push computing to the emerging area of
cognition and creativity \cite{KellyH2013}.

Variety is about trying to capture all of the data that pertains to the cognitive process within the creative domain and
also outside of it.  Since most data is semistructured or unstructured, making sense of it is not natural to computers and
requires new ways of data engineering and data management.  What is required is a holistic approach that allows
the ability to mash together different kinds of data and act on them to create artifacts which have never been imagined.

Veracity refers to quality or trustworthiness of data.  Since much of the data we have access to is not complete, precise,
or certain, see e.g.\ \cite{VarshneyVWM2013}, we must embrace analytics algorithms that are robust so as to allow drawing
the right conclusions.

The sheer volume of intermediate data that is generated as part of computational creativity is a challenge in itself.
Moreover, operating at the velocity of human thought is crucial for a computational creativity system to serve as a
true colleague to human creators and to quicken the product design cycle.  Efficient computer architectures and paralellizable
algorithms can help mitigate both of these concerns.

\section{Summary, Results, and Outlook}
\noindent{\bf Summary} In this paper, we have developed a computational creativity system that can automatically or semi-automatically design and
discover culinary recipes that are flavorful, novel, and perhaps healthy.  This is done through artificial intelligence
algorithms based on Bayesian probability and regression analysis that draw on big data techniques, as well as disparate data sources
from culinary traditions, chemoinformatics, and hedonic psychophysics.  We proposed a structure for a computational creativity system
that contains three main components: a designer, an assessor and a planner, all fed by a domain knowledge database.
Furthermore, we discussed the role of the domain knowledge database in structuring and setting the bounds for cognitive processing.
\vspace{2mm}

\noindent{\bf Results} Recipes created by the computational creativity system, such as a Caymanian Plantain Dessert, have been rated as more creative
than existing recipes in online repositories using a method similar to the Consensual Assessment Technique \cite{Davis2013}.
Moreover, professional chefs at various hotels, restaurants, and culinary schools have indicated that the system helps them
explore new vistas in food.  These results provide validation for the data-driven approach to computational creativity.
\vspace{2mm}

\noindent{\bf Outlook} Creativity is easy neither for people nor for machines, but the challenges are different.  Without taking advantage of
modularity, people often have trouble being creative and innovative because they are overwhelmed by the combinatorial
complexity of large design spaces \cite{McNerneyFRT2011}.  Since people end up thinking modularly, progression of creative
thought is often evolutionary \cite{Basalla1988}.  A computational creativity system can test quadrillions of ideas at once
without needing to invoke modularity and may thus offer solutions that completely redefine an art.  Such creations may offer
advantages by being completely `outside the box' through large jumps in thought rather than gradual evolutionary changes.

Although we took a particular creative application domain---culinary recipe design---as an example, the system architectures, approaches,
and insights garnered in facing the challenges should be applicable across creative domains.

\section*{Acknowledgment}
The authors thank Lamia Tounsi, Krishna Ratakonda, Jun Wang, EE Jan, and Terry Heath from IBM, as well as
James Briscione and Michael Laiskonis from the Institute of Culinary Education.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


The Statistics Office is conducting a Business Survey from October to mid-November 2018. The last survey of this type was conducted in 2010.
This is an important survey to better understand St Helena's economy, the current business environment, and St Helena's labour market. It is a long time since businesses were surveyed in this way, and St Helena has been through a period of tremendous change since then. It's especially important now that scheduled air services are operating, and a new freight service has been introduced. The analysis from the statistics, arising from the data collected from this survey, will play an important part in decision-making; therefore I would encourage businesses to take part.
The survey fieldwork will start on Monday, 8 October, when questionnaires will be delivered. The survey is expected to take around six weeks, and it will be conducted within the legal framework of the 2000 Statistics Ordinance. All businesses with employees (i.e. apart from self-proprietors) are required to complete and return the questionnaire. Statistics Officers will collect completed questionnaires, and will give businesses any help they need.
The survey includes around twenty questions in areas related to the business environment, employment & staff recruitment, leave & benefits, and staff training. A further section on income and expenses will only be required of larger businesses (those who employ ten people or more). The information provided on the questionnaires will be treated with strict confidence, and will be used only for the purpose of compiling statistics, in accordance with the Statistics Ordinance.
Our job in the Statistics Office is to help people make better decisions; good data does not guarantee good decision-making, but it does make it much more likely. I'd like to thank all businesses for their cooperation and help with the survey � we've tried hard to make sure the survey is straightforward and simple for businesses to complete, but the enumeration team will be available to help if needed. Please contact us on tel: 22318 or by email: statistics@sainthelena.gov.sh, or visit our office in the Castle, if you have any questions or comments, or need help with the survey. We'll be more than happy to try and help to make the process as painless as possible.


The Young People Count report contains a brief guide to the different data sources that are available on the problems that young people face in the United Kingdom. Drawn from official figures, such as the Office of National Statistics, and independent research by community and voluntary organisations, it covers the subjects of health, youth employment, housing, crime, education, participation, poverty, and general population data.
What is interesting, and the source of possible difficulties in youth policy, is the overlap and interaction between each of these problems. The interlinked nature of these topics like housing, education and poverty make the situation incredibly complex. Yet, the only thing that is clear is the difficulties and inequality young people still face.
On the topics of employment, housing, poverty and crime – young people are affected greater than the rest of the population. Facing discrimination, worse employment opportunities and more crime, young people have been the worst impacted by the recession. This, coupled with a lack of affordable housing, makes one of the hardest situations facing the next generation to face in decades.
Download the data report Young People Count.


You are on the mfp page in the user guide search section. In order to find user guide for your mfp you have to choose manufacturer from the catalog.
If you didn't find your device manufacturer in the catalog, then probably we don't have given user guide download for mfp, but you can leave us a demand by going on the File Request page. Our staff will endeavour to find the desired user guide for mfp and will add user guide to the database, then notify you by e-mail.


text:
Essex National Heritage Area
Trustees and Commissioners
Corporate Sponsors & Members
National Heritage Areas
Join / Donate
Byway
Opening Day for Boat Tours to Bakers Island Light Station: Saturday, June 17!
On June 17, Essex Heritage will kick-off the summer season with guided boat tours to Bakers Island Light Station. For twelve weeks only, from June 17 through September 4 (Labor Day), the military style landing craft Naumkeag will disembark passengers on the beach at Bakers Island. From here, visitors can take a guided tour of the property, learn about its unique history, hike newly cut trails, play croquet and bocce, have a picnic, or just relax and enjoy the view.
"We are delighted to open up the light station for the third season and to welcome the new volunteer light keepers, Dave and Janet" said Annie C. Harris, Essex Heritage CEO. "We have new trips and experiences planned for this summer including lobster tours, en plain air painting trips, and wild edibles hikes. As a special pre-opening-day voyage, we are offering to take 18 passengers to see the Tall Ships arrive in Boston on Friday, June 16."
Regular trips to Bakers Island Light leave twice a day from the dock adjacent to the Salem Ferry Landing. Trips depart at 10:30 AM and 2:00 PM on Friday, Saturday, and Sunday for this 2 ½ hour adventure. The Salem Ferry Landing is located at 10 Blaney Street, Salem MA, just past the House of the Seven Gables. Free parking is available next to the dock.
Bakers Island Light Station is ONLY accessible to the public through these tours with Essex Heritage. Naumkeag is a large, open boat with a bow front that drops down so passengers can walk directly onto the stone beach (not landing at a dock). No private landings are allowed.
Don't miss the boat! Tickets are available for purchase online at www.EssexHeritage.org/boat, and at the National Park Service's Regional Visitor Center (2 New Liberty St, Salem).
Essex National Heritage Area • 10 Federal Street, Suite 12, Salem, MA 01970 • ph 978-740-0444, f 978-744-6473

redpajama_set_name: RedPajamaCommonCrawl


text:
2023 Nissan Pathfinder "Rock Creek" Review – Auto Trends Magazine
by Effie Weber November 18, 2022 039
Today's family livery is often a three-row utility vehicle, represented by more than one dozen crossover models. The Nissan Pathfinder is in the thick of things, a model that seats up to eight and delivers class-leading towing capabilities.
For 2023, the Pathfinder gains a Rock Creek edition, a special grade with improved off-road facilities and a tough look to go with it.
2023 Nissan Pathfinder Review
Nissan offers the 2023 Pathfinder in five grades – S ($35,000), SV ($37,820), Rock Creek ($42,820), SL ($42,420), and Platinum ($47,970).
Front-wheel drive is standard; all-wheel drive is a $1,900 upgrade for all trims, yet it is included with the Rock Creek. Add $1,295 for the destination charge.
Our test model was a 2023 Nissan Pathfinder Rock Creek grade with standard all-wheel drive. This model is well optioned with select individual upgrades available including two-tone paint ($795), running boards ($615), and illuminated kick plates ($325).
With a final price of just over $44,000, the Pathfinder costs less than the competing Kia Telluride and Hyundai Palisade.
Exterior Remarks
Manufacturers routinely roll out special packages that sometimes become standalone trims or grade levels. That is exactly what Nissan did with the Pathfinder Rock Creek.
The new grade sits squarely in the middle of the Pathfinder's five-model lineup. We've written extensively about the latest and fifth-generation Pathfinder with reviews you can find here and here. For the sake of this review, we'll examine what Rock Creek brings to the model line.
The Rock Creek edition brings in a more nature-pursuing look for the Pathfinder. Ever since the Pathfinder switched to a crossover design in 2013 and the Xterra was dropped, the brand has lacked a true off-road credibility SUV to slot below the range-topping Armada.
The Rock Creek doesn't quite bridge that divide, but it does gain a half-inch advantage in ground clearance as well as improved approach, breakover, and departure angles.
However, do not think for a moment that you'll follow the Jeep Wrangler or Ford Bronco into the hinterlands even though the Rock Creek comes with Beadlock wheels wrapped within all-terrain tires. Notably, Rock Creek lacks the sturdy underbody cladding or skid plates necessary for protecting the transmission, oil pan, fuel tank, and other vulnerable and exposed parts.
What Rock Creek does have is a raised safari-type tubular roof rail system with a 220-pound capacity, special badging, unique bumpers, and grille treatment.
At this grade level, the Pathfinder features LED fog lights. Curiously absent is a power liftgate. And just like the base grade, it isn't even on the options list. This deficiency may be a showstopper for anyone who finds the liftgate too heavy or that it soars too high to their liking.
Interior Highlights
The Rock Creek treatment comes inside and in the form of front seat debossing and special imitation leather-covered seating surfaces dressed in black. Also, this is the only grade with standard seven-passenger seating as Nissan includes second-row captain's chairs. Unique styling, including orange piping, set this model apart from the pack.
The Pathfinder's interior is as roomy as most competitors with very comfortable seats in the first two rows and sufficient space for three in the third row. Nissan equips the Pathfinder with numerous storage compartments, including drink holders, cubbies, and concealed storage.
Middle row captain's chairs
Behind the third row is 16.6 cubic feet of storage space. Although small, that's more than the 14.4 cubic feet offered by the Mazda CX-9.
Fold down the third-row seat and the storage capacity mushrooms to 45 cubic feet. Behind the third row, 80.9 cubic feet of space is available. That's only 10 percent more space than the compact Nissan Rogue.
All grades come with full power accessories, a tilt-and-telescopic steering column, and three-zone climate control. If you want full leather seating, then the SL and Platinum models have you covered.
Technology Features
An 8-inch touchscreen display screen and a 6-speaker audio package come standard. Satellite radio, Bluetooth, Apple CarPlay, and Android Auto are included. Every grade has at least four USB ports.
Move up through the grade range and the Pathfinder features a 9-inch touchscreen display with navigation, a Wi-Fi hotspot, and wireless device charging. A 13-speaker Bose audio package is also available along with two additional USB ports.
Nissan also makes available a 12.3-inch digital gauge cluster and a 120-volt power outlet.
Nissan equips the 2023 Pathfinder with forward and reverse collision warning and automatic emergency braking. Automatic high-beam headlights, blind-spot monitoring, rear cross-traffic alert, pedestrian detection, and lane-departure warning are included.
Also available are front parking sensors and a surround-view parking camera system. A head-up display and ProPilot Assist are also available. The latter bundles adaptive cruise control with lane centering to keep this SUV on track when activated.
Powertrain Choices
One year removed from its most recent overhaul, the single powertrain combination offered in 2022 carries forward. That's a 3.5-liter V6 engine with 284 horsepower and 259 pound-feet of torque. In Rock Creek models, performance tweaking ups those numbers to 295 horsepower and 270 pound-feet of torque.
Power routes to the wheels utilizing a 9-speed automatic transmission. When properly equipped, the Pathfinder pulls up to 6,000 pounds. This compares to upwards of 5,600 pounds in the Ford Explorer and no more than 5,000 pounds in the Volkswagen Atlas.
Models equipped with front-wheel drive earn an EPA-estimated 20/27/23 mpg city/highway/combined. Choose all-wheel drive and Pathfinder delivers 20/25/22 mpg. However, the Rock Creek trails the pack with 20/23/21 mpg.
Driving Highlights
We didn't push our test model to the limits, but we did tackle nearby farmland and went up and down each path with ease. We appreciated the extra grip afforded by the all-terrain tires and immediately noticed the difference in the limited-slip differential supplied under certain conditions.
Indeed, up to 50 percent of the power from the front wheels moves rearward, with an extra amount to the wheel that needs it most.
Figure that if you're turning around and the conditions are muddy, you'll escape the morass. Although the Pathfinder lacks a true low gear, it does come with a drive mode system designed to tackle mud, sand, and snow. We would simply avoid any place where conditions are less than favorable.
On the other hand, the Pathfinder will see far more usage on the road by carrying families to soccer games, church, school, and family outings. The Pathfinder delivers strong off-the-mark acceleration with ample power delivered to the wheels.
We're happy that Nissan deep-sixed the previous CVT in favor of a conventional geared transmission. For the most part, the transmission works as expected although downshifts may occasionally lag.
On the highway, the Pathfinder moves with authority, offers a comfortable ride, and supplies firm braking. Precise steering and good handling are two other hallmarks of this model.
Nissan Pathfinder Considerations
The Rock Creek model brings off-road chops to the Pathfinder. It does so with a unique style and includes features that set this model apart from the pack.
But if you're shopping for an SUV and have no intention of off-roading, the Pathfinder SL is similarly priced and includes the power liftgate not offered by the Rock Creek. Add in all-wheel drive and your final price should stay below $45,000 or within the range of competing models.
Photos copyright Auto Trends Magazine. All rights reserved.
Owner at Auto Trends Magazine
Steve is a former ASE Certified Technician and holds a degree in automotive service technology from Weber State. When he's not searching for a 10mm socket in his garage, he enjoys a good BBQ with family and friends.
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Effie Weber August 9, 2022


This thing is great looking. Doesn't say if it's been restored or if it's all original. Has the tall back window. Not sure about the price, seems a tad high, but that kinda depends what it looks like underneath and whether or not it's all original. If it is original, good luck finding a nicer one of these in original shape. Almost looks new.
Location: Southern Sweden - Sturkö island Wow!!!! Very nice car, hope someone here buys it!
Location: Lower Mainland BC Beautiful interior. 383. What's not to like?
Location: Victoria, BC, on Vancouver Island, Canada OK there are a couple of issues with this car that aren't right. The interior is PURE Matador, not Polara. SO either the car has been re-badged as a Polara, OR the car is a Canadian built Polara which featured the Matador interior. Would like to see the VIN plate and the Data tag to verify. Also the cloth inserts are not stock and the the way they are sewn in is close to stock Matador, but not quite stock.
The High back window was standard on the Polara 4 door HT, and optional on the Matador 4 doot HT. The rear quarter panel washboards were optional on both. The Fin end lenses were standard on Polara and N/A on Matador although many have been retro-fitted on Matadors. It also has Canadian style wheel covers which were not quite the same as the US versions, even without the spinner bars. The front fender top ornaments are also standard for Polara. He states that the engine is a 383, but the Canadian Polara came with a 361.
My best guess is that this is a Canadian built car, and being in close proximity to Washington State, made it's way there at some point.
It's a nice car either way but be on guard as to what it really is. I've emailed the seller to see if he will give me the VIN and Data tag info.
Location: Pacific Northwest Ad is gone.
ABloch - 2018-11-07 8:32 PM Ad is gone.
That was fast. Guess the price was good.
Location: somerville mass I bought it after some haggling for 22k sight unseen car was bought new in Vancouver bc.
Location: Victoria, BC, on Vancouver Island, Canada Awesome Steve, Are you having it shipped back to MASS? If you need any help with Canadian 60 Polara specs I'm the guy to ask.
Location: Southern Sweden - Sturkö island Great catch Steve, the flaws are all possible to fix. I really like the 60 Dodges and especially the Polaras.
Location: somerville mass it will be a driver taken to shows in spring I have no storage ordered a car cover cant wait for the attn I get with this thing!!!!!!!!!!!!!!!
Location: Victoria, BC, on Vancouver Island, Canada I owned a 1960 Canadian built Polara from 1974 through until 1995. It's now in the hands of a good friend of mine. The first time I took it to a WPC National Meet, in 1980 in Seattle, club members were talking behind my back about it, saying that I had re-badged a Matador into a Polara, simply because of the interior. One of the other meet attendees told me what was being said, and I soon enlightened these other individuals as to the fact that the Canadian production Polaras were built that way and that no 1960 Matadors were built for sale in Canada.
Ian, I think by now everyone who likes these cars knows you are one of the people that used to own one at one time, now that one is a beauty too, you took good care of it!
If it ever comes up for sale, please let me know.
smooth quiet heat works great where do I get or go for disc brake conv.
moparsteve - 2018-11-11 4:04 PM got it late last nite blown away by the color and space age styling and that clock! drove it around boston area wont believe the attention ii got MIT student wanted to buy it everyone waved blew their horn!!!!! ill keep it covered during snow smooth quiet heat works great where do I get or go for disc brake conv.
Yeah there's nothing quite like a 60 Dodge, and yours is probably one of the best looking I've ever seen for sale. Any word on whether or not it was all original or a restoration?
Dream on. The buy it now button for that 300C on eBay is gone (now). It is on auction. Not likely to go for your Polara money.
Christmas came early at my house! Its very seldom a car comes nicer then described, this ones over the top.
Location: northern germany So who bought the car???
Location: Lordstown, Ohio Mark did. The guy pictured with the car.
So did the sale fall through with Mopar Steve? What happened here?
Kind of strange as Steve is a regular on this board, and talked about having bought this car in several of the threads above! ! ! ? ?


In eBay's media marketplace, buyers will be able to submit Web forms akin to requests for proposals specifying what dates they want to run ads, what their budget is, target demographics they seek in the audience and other information. They then invite networks to review their proposal. Sellers also will have the option to initiate auctions, following the eBay model, where they specify the ad space they have available to sell and choose the highest bidder. The same model as ebay's main buying and selling model.


text:
Q: How use webview history back in a fragment? I thought it was a very easy problem... but I haven't solved for quite some time.
For now, pressing back button will turn off the app. I want to go back by pressing the back button.
Fragment containing the webview.
HomeFragment.java
public class HomeFragment extends Fragment {
    WebView webView;

    public HomeFragment() {
        // Required empty public constructor
    }

    @Override
    public View onCreateView(LayoutInflater inflater, ViewGroup container,
                             Bundle savedInstanceState) {
        // Inflate the layout for this fragment
        return inflater.inflate(R.layout.fragment_home, container, false);
    }

    @Override
    public void onViewCreated(View view, @Nullable Bundle savedInstanceState) {
        super.onViewCreated(view, savedInstanceState);
        webView = view.findViewById(R.id.home_webview);
        String url = "https://galaxyhub.kr/starcitizen";
        webView.loadUrl(url);
        webView.getSettings().setJavaScriptEnabled(true);

        final ProgressDialog dialog = ProgressDialog.show(getActivity(),"", "Loading", true);
        webView.setWebViewClient(new WebViewClient(){
            @Override
            public void onReceivedError(WebView view, WebResourceRequest request, WebResourceError error) {
                Toast.makeText(getActivity(), "URL ERROR", Toast.LENGTH_SHORT).show();
            }

            @Override
            public void onPageStarted(WebView view, String url, Bitmap favicon) {
                dialog.show();
            }

            @Override
            public void onPageFinished(WebView view, String url) {
                super.onPageFinished(view, url);
                dialog.dismiss();
            }

        });
    }
}

I heard that I should also write in the MainActivity.
I'll post it right away if needed.
package com.example.starcitizen.fragment;

import android.app.Activity;
import android.app.ProgressDialog;
import android.graphics.Bitmap;
import android.os.Bundle;
import android.view.LayoutInflater;
import android.view.View;
import android.view.ViewGroup;
import android.webkit.WebResourceError;
import android.webkit.WebResourceRequest;
import android.webkit.WebView;
import android.webkit.WebViewClient;
import android.widget.Toast;

import androidx.activity.OnBackPressedCallback;
import androidx.annotation.Nullable;
import androidx.fragment.app.Fragment;

import com.example.starcitizen.R;

/**
 * A simple {@link Fragment} subclass.
 */
public class HomeFragment extends Fragment {
    WebView webView;

    OnBackPressedCallback callback = new OnBackPressedCallback(true) {
        @Override
        public void handleOnBackPressed() {
            if (webView.canGoBack()) {
                webView.goBack();
            } else {
                //use this if you need to call the Activity
                //backpress for default back action
                requireActivity().onBackPressed();
            }
        }
    };
    @Override
    public void onCreate(@Nullable Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        requireActivity().getOnBackPressedDispatcher().addCallback(this, callback);
    }

    @Override
    public View onCreateView(LayoutInflater inflater, ViewGroup container,
                             Bundle savedInstanceState) {
        // Inflate the layout for this fragment
        return inflater.inflate(R.layout.fragment_home, container, false);
    }

    @Override
    public void onViewCreated(View view, @Nullable Bundle savedInstanceState) {
        super.onViewCreated(view, savedInstanceState);
        webView = view.findViewById(R.id.home_webview);
        String url = "https://galaxyhub.kr/starcitizen";
        webView.loadUrl(url);
        webView.getSettings().setJavaScriptEnabled(true);

        final ProgressDialog dialog = ProgressDialog.show(getActivity(),"", "Loading",
                true);
        webView.setWebViewClient(new WebViewClient(){
            @Override
            public void onReceivedError(WebView view, WebResourceRequest request, WebResourceError error) {
                Toast.makeText(getActivity(), "URL ERROR", Toast.LENGTH_SHORT).show();
            }

            @Override
            public void onPageStarted(WebView view, String url, Bitmap favicon) {
                dialog.show();
            }

            @Override
            public void onPageFinished(WebView view, String url) {
                super.onPageFinished(view, url);
                dialog.dismiss();
            }

        });
    }
}

Now this code is running!

A: Add the fragment to the back stack if you need to navigate back from your fragment when the WebView can't go back more.
getSupportFragmentManager().beginTransaction()
  .replace(R.id.fragment_container, fragment)
  .addToBackStack(null)
  .commit();

Then in your fragment:
public class HomeFragment extends Fragment {

    private WebView webView;

    @Override
    public void onAttach(@NonNull Context context) {
        super.onAttach(context);
        OnBackPressedCallback callback = new OnBackPressedCallback(true) {
            @Override
            public void handleOnBackPressed() {
                if (webView.canGoBack()) {
                    webView.goBack();
                } else {
                    //use this if you need to call the Activity
                    //backpress for default back action
                    requireActivity().onBackPressed();
                }
            }
        };
        requireActivity().getOnBackPressedDispatcher()
             .addCallback(getViewLifecycleOwner(), callback);
    }

    @Override
    public void onViewCreated(View view, @Nullable Bundle savedInstanceState) {
        super.onViewCreated(view, savedInstanceState);
        webView = view.findViewById(R.id.home_webview);
        webView.getSettings().setJavaScriptEnabled(true);
        webView.setWebViewClient(new WebViewClient() {
            //callbacks omitted...
        });
        String url = "https://galaxyhub.kr/starcitizen";
        //call this after WebViewClient setup
        webView.loadUrl(url);
    }
}

Remember that Activity on onBackPressed will be called before the one added in the Fragment.
More info on back press handling here: https://developer.android.com/guide/navigation/navigation-custom-back#java
For this to work use at least:

androidx.fragment:fragment:1.1.0

redpajama_set_name: RedPajamaStackExchange


Show off your inner princess with our modern luxurious gowns that have lavishly chic designs in an array of colors and rich fabrics to choose from to give you that undeniable royal sparkle. Let Mestad's help you shine bright, and show off your inner sparkle with these stellar ball gowns, fit for a princess.
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text:
Q: Yahoo UI the popup implementation with YAHOO.widget.Dialog I used the Yahoo.widget.dialog to implement a popup window. Currently, it popups only on the page which is belongs. If I am working on the other (tab browser) pages. The popup window will be not seen.Could somebody supply some help for this ?Thanks

A: You could either switch to window.alert() or use window.open() to open a new pop-up window containing your content.
Neither one is something I'd personally want to experience, but those are your two options for opening a pop-up outside the page. Window.open will get caught by many pop-up blockers so you'll need to watch for that.


text:
Category: bollywood
Director Pradeep Sarkar tackles a sensitive yet highly provocative subject in his latest Bollywood film, Mardaani. Receiving a whopping 15 crore (which is around 1496264.08 pounds) in its three day box office, Mardaani seems to have had a positive outcome, making it a film not to miss. Mardaani focuses on the subject of child trafficking in India. The film follows the cold fact of underground … Continue reading Mardaani (2014) Film Review
meeradarji August 28, 2014 December 31, 2014 1 Comment
Holiday (2014) Film Review
Director, A.R Murugadoss brings to us a exciting action film starring Bollywood's favourite; Akshay Kumar and romance special; Sonakshi Sinha. Captain Viraat Bakshi (Akshay Kumar), member of the Indian Army travels back home to visit his family for his holiday. Yet, his reunion with his family begins with being dragged to see a girl for marriage (because that's just normal in India). Sitting there in … Continue reading Holiday (2014) Film Review
meeradarji August 14, 2014 December 31, 2014 4 Comments
Queen (2014) Film Review
Queen is a new coming of age Bollywood film which takes you on a delightful journey of an innocent young girl, Rani (Kangana Ranaut). With this being the second film directed by Vikas Bahl you can't help but get glued to where this tale takes you. As the bride, Rani gets ready for her glamorous wedding, she dances her way with her family celebrating the … Continue reading Queen (2014) Film Review
bollywood, Uncategorized
Dhoom 3 (2013) Film Review
With a hit films Dhoom and Dhoom 2, Vijay Krishna Acharya brings us another super fast, action sequence in Dhoom 3. Releasing around Christmas time, in only 8 days, Dhoom 3 broke the Bollywood film record of crossing the 100 crore mark at the overseas box office. Acclaimed to be one of the top grossing films of 2013, Dhoom 3 is definitely one not to … Continue reading Dhoom 3 (2013) Film Review
meeradarji December 28, 2013 May 12, 2014 3 Comments
Goliyon Ki Rasleela Ram-Leela (2013) Film Review
Inspired by Romeo and Juliet's unforgettable tale, Sanjay Leela Bhansali brings us a new take to the love story in Goliyon Ki Rasleela Ram-Leela, short for Ram-Leela. In a small city of Ranjaar based in Gujarat, two opposing tribes; Rajadi and Sanera survive by thrusting hate on each other and approaching violence. The Rajadi's and Sanera's have this strong enmity in which has been lasting … Continue reading Goliyon Ki Rasleela Ram-Leela (2013) Film Review
meeradarji December 2, 2013 May 12, 2014 7 Comments
Bhaag Milkha Bhaag (2013) Film Review
A moving biopic based on a true runner 'Milkha Singh' also known as 'The Flying Sikh'. Directed by Rakeysh Omprakash Mehra and starring Farhan Akhtar as Milkha Singh, Bhaag Milkha Bhaag takes you on an emotional journey. The truth behind Singh's journey gradually unfolds, where we see his plight, struggle and constant hard work. After facing the India-Pakistan war and losing close family members, Milkha … Continue reading Bhaag Milkha Bhaag (2013) Film Review
meeradarji November 24, 2013 December 28, 2013 1 Comment
Chennai Express (2013) Film Review
Rohit Shetty once again creates a successful rom-com which wins our hearts. A sweet love story with sparks of comedy ticks the entertainment boxes. The films tagline 'Love has no language' is the perfect four words to describe this film. 40 year old Mithai Wala, Rahul (Shak Rukh Khan) is tired of working in his Dadajee's sweet shop. He's getting older and simply wants to … Continue reading Chennai Express (2013) Film Review
meeradarji August 19, 2013 November 24, 2013 Leave a comment
Aashiqui 2 (2013) Film Review
Rahul Jaykar, the ultimate star is India's favourite singer. As the stadium chants his name, Rahul indulges in his alcohol. Rahul is at the point in his life where fame, money and talent are at stake. With millions of fans drooling for RJ, all he drools for is alcohol. Indeed, an addiction which Rahul struggles to let go of. After crashing into a dark gully … Continue reading Aashiqui 2 (2013) Film Review
meeradarji August 7, 2013 November 24, 2013 Leave a comment
Yeh Jawaani Hai Deewani (2013) Film Review
Ayan Muckerjee's latest Bollywood flick Yeh Jawanni Hai Deewani has somewhat left me with a mixed reaction. A romance drama based on the life of the crazy young people yet fails to capture a mesmorising story? Naina (Deepika Padukone) the biology geek and simple girl is fed up of her boring life. Bumping into her long lost friend Aditi (Kalki Koechlin), Naina realizes what fun … Continue reading Yeh Jawaani Hai Deewani (2013) Film Review
Singham (2011) Film Review
Director Rohit Shtty sure brings a roar with his moving action, drama film 'Singham'. From exploring real Indian Politics to impressive bravery, Singham is a true eye opener and a must-watch for all. The film follows the brave Inspector Baijiro Singham (Ajay Devgn) whom aims to bring peace and justice to his small village. However, with cunning con men and dirty politics in place, innocent … Continue reading Singham (2011) Film Review
meeradarji July 23, 2013 August 6, 2013 2 Comments
Murder 3 (2013) Film Review
An intense, slow paced thriller, which definitely over-runs with time and lacks that core grittiness. Directed by Vishesh Bhat, Murder 3 has some surprising twists yet overall fails to achieve the full works of a thriller. The film follows the life of Vikram (Randeep Hooda), a fashion photographer whom is in love with his perfect women, Roshni (Aditi Rao Hydari). After moving together in a … Continue reading Murder 3 (2013) Film Review
meeradarji July 13, 2013 July 26, 2013 Leave a comment
Dostana (2008) Film Review
Dostana (friendship) is an absolute hilarious film, focused on two great lead actors; Abhishek Bachan (Sameer or 'Sam') and John Abraham (Kunal). Director, Tarun Mansukhani definitely entertains the audience and finally focuses on a modern concept in a Bollywood Film. The film follows the life of Kunal and Sam, a friendship which forms from a quite realistic lie. Their desperation of wanting an apartment in … Continue reading Dostana (2008) Film Review
meeradarji July 4, 2013 July 26, 2013 Leave a comment
ABCD: Andy Body Can Dance (2013) Film Review
Bollywood's latest dance film 'ABCD' (Any Body Can Dance) brings out the true spirit throughout. Starring Prabhu Deva, as the lead actor, the film is filled with a fun storyline, taking dance to another level. Director Remo D'Souza, creates a stunning film with mind blowing visuals, gradually moving the audience. ABCD follows a predictable storyline, in which I was slightly disappointed due to lack of … Continue reading ABCD: Andy Body Can Dance (2013) Film Review
meeradarji June 15, 2013 August 6, 2013 2 Comments
Bhool Bhulaiyaa (2007) Film Review
Priyadarshan unites a gripping tale with a mysterious story, leaving the audience mesmorised. Bhool Bhulaiyaa truly grasps an enriching adventure, where your focus is fixed solely on the film. The climax beautifully builds up, where the cast powerfully stabilizes the story. Bhool Bhulaiyaa follows the story of a creepy ghost, Manjulika, a long lost princess, a soul which is trapped in the very mansion. The … Continue reading Bhool Bhulaiyaa (2007) Film Review
meeradarji June 14, 2013 July 4, 2013 Leave a comment
Om Shanti Om (2007) Film Review
Om Shanti Om, a Hindu spiritual chant defining peace for all kind. A unforgettable Bollywood blockbuster directed by the famous Farah Khan, which brings true love, humanity, friendship, honesty and dreams to life. With a deserving 13 wins and whopping 14 nominations, Om Shanti Om is worth a watch for all the right reasons. Warning, may contain spoilers. Set in the 1970's, an aspiring actor … Continue reading Om Shanti Om (2007) Film Review


Imagine the look on your little one's face when a life-sized version of their favorite character walks into their party! Booking a costumed character can bring a wow factor that is unmatched. Popular characters from television, movies, and books are all available on GigSalad, and waiting to light up the party! Costumed characters are ready for hugs, dancing, photo ops, and more with your kiddo. Plan a party that they'll never forget with a costumed character! Search for Costumed Characters in Covington, Kentucky above.
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text:
Masjid Jamek es una de las más antiguas mezquitas de Kuala Lumpur (Malasia), localizada en la confluencia de los ríos Klang y Gombak, y fue diseñada por Arthur Benison Hubback.

Cuando el Sultán de Selangor abrió de manera oficial la mezquita en 1909, la construcción de la misma fue culminada dos años después; edificada en el primer cementerio malayo de la ciudad. De la msma manera, antes de que la mezquita nacional, Masjid Negara, fuera abierta en 1965, Masjid Jamek era la principal mezquita de Kuala Lumpur.

La mezquita está construida con un diseño moro. Al otro lado del Klang se encuentra el Edificio del Sultán Abdul Samad (Sultan Abdul Samad Building), realizado por el mismo arquitecto con un diseño similar. Masjid Jamek es también el nombre de estación del Metro de Kuala Lumpur; la mezquita se encuentra además cerca a Dataran Merdeka.

Enlaces externos

Jamek
Mezquitas del siglo XX
Edificios religiosos terminados en 1909
Arquitectura de Malasia del siglo XX
Arquitectura indo-sarracena

redpajama_set_name: RedPajamaWikipedia


The match of FK Borac Sakule vs FK Radnicki Zrenjanin in Serbia Srpska Liga is started at 2019-3-24 21:59. For this match, the initial Asian Handicap is FK Borac Sakule0.0; The initial Goals Odds is 2.5.
For the last 15 matches, FK Borac Sakule got 7 win, 7 lost and 1 draw with 22 Goals For and 18 Goals Against. The average of Goals For is 1.5 per match and the average of Goals Against is 1.2 per match. The rate of Over Goals is 63%; The rate of Handicap Win is 47%; The rate of win is 47%; The average of corners is 3.5 per match and the rate of Over Corners is -.
For the last 15 matches, FK Radnicki Zrenjanin got 5 win, 5 lost and 5 draw with 21 Goals For and 22 Goals Against. The average of Goals For is 1.4 per match and the average of Goals Against is 1.5 per match. The rate of Over Goals is 73%; The rate of Handicap Win is 37%; The rate of win is 33%; The average of corners is 4.5 per match and the rate of Over Corners is -.
Except the history stats of FK Borac Sakule vs FK Radnicki Zrenjanin, ScoreBing also offers predictions and lineups of FK Borac Sakule vs FK Radnicki Zrenjanin, that may help you predict or replay the match.


text:
Giant bunnies are back in Charlotte, prompting selfies and even a wedding
WFAE | By Dashiell Coleman
Half of the giant bunnies of the "Intrude" public art installation are seen Sept. 29 near First Ward Park in uptown Charlotte. They'll stay up until Oct. 12.
This was supposed to be the last week of the Charlotte SHOUT! Festival. But the celebration of diversity and creativity was postponed because of the pandemic for the second year in a row. There's a pretty big consolation prize, though — a dozen of them, in fact, with floppy ears to boot.
"Intrude," a public art installation composed of 12 large, inflatable rabbits that light up at night, jumped to life Sunday in a field across from First Ward Park in uptown, just off of East Eighth Street.
"It's been really fun," said Bree Stallings, director of artistic experiences at the Blumenthal Performing Arts Center. "On the first night, we had probably over 600 people come before we had really started even announcing it — while the rest of the bunnies were still being blown up."
When the festival came back in 2019 after a 10-year break, "Intrude" was one of its most popular attractions. Blumenthal is a major sponsor of the festival and had already secured the installation for 2021 by the time SHOUT! was called off.
"It had a huge response," Stallings said. "It brought a lot of people out, and there's a bunch of joy that happens when people get to see large-scale artwork — especially stuff that lights up. And it really ignites their inner child and makes room for a moment of play across generations."
Now the rabbits are back, but there are more of them this time.
Y'all...they're MULTIPLYING!🐰
Can you spot the FOUR new @parerstudio Bunnies we added to First Ward Field this morning?
They look great alongside the "As strong as the beat of my heart" Of Earth and Sky poetry installation! To read the full poem, visit: https://t.co/SrzASNdKLm pic.twitter.com/cmqXcA5I65
— Blumenthal Performing Arts (@BlumenthalArts) September 30, 2021
The installation was created by Australian artist Amanda Parer. "Intrude" comes in all shapes and sizes — the Parer Studio site shows options ranging from small (aka as big as a pony) to XXL (aka, bigger than some houses). And they've traveled to cities across the world, including Rome, Shanghai and Mexico City.
"Intrude" is meant literally, according to the studio. The bunnies are "invading" their host cities in a reference to how rabbits were introduced to Australia in the 1800s and have become an invasive species. They've actually wreaked some ecological havoc there, harming native species and crops,National Geographic reports.
But, you know, rabbits are also cute.
"'Intrude' deliberately leans on this cutesy image coupled with a strong visual humor to lure the audience into the artwork only to reveal the more serious environmental messages which form the basis of the work,"Parer Studio says on its site. "They are huge, the size referencing 'the elephant in the room,' the problem, like our impact on the natural world, big and in plain sight, but still chosen to be ignored."
The "Intrude" bunnies are definitely too big to ignore if you're anywhere near First Ward Park. The largest one is just over 35 feet tall.
"There's something about the scale and the magnetism of those things that get people out of their cars, running across the field and throwing their arms up to take a picture," Stallings said.
Elizabeth Palmisano and Krystle Baller were so excited when they found out the bunnies were coming back that they got married in front of them. The pair had just started dating in 2019 when they went to the SHOUT! Festival — and that's where they say they fell in love. "Intrude" played a big part in those memories, and the couple's original wedding plans got disrupted by the pandemic.
So, when they found out the bunnies were coming back, they set up a flash wedding.
"There were children and adults running around the bunnies just playing, not taking themselves too seriously," Palmisano said. "And I think that's a really special kind of magic, and I really wanted the space we got married in to feel as magical as our relationship feels."
They tied the knot Wednesday evening. The moment lived up to their expectations.
"There were so many random people out there and everybody was just smiling from ear to ear, and it was really cool," Baller said. "... It was just really neat for strangers to walk up and see a public proclamation of love."
Swing by in the mornings and you'll see families with young children meandering through the installation and taking photos. At night, the rabbits glow, with Charlotte's skyline lit up behind them.
"Intrude" isfree to visit, and it's open from 10 a.m. to midnight through Oct. 12.
It's worth noting there are a few safety rules for the enormous rabbits — common sense stuff like no hitting them, jumping on them, trying to crawl under them. As the "bunny rules" on a sign at the installation puts it, "treat them like real bunnies."
Especially the one that's the size of a 3-story house.
Another thing: There are two other public art installations intended for SHOUT! that you can see now. "GAIA" by Luke Jerram is a 22-foot-diameter rendition of Earth that's suspended at Founders Hall on North Tryon Street. It comes down tomorrow. "Of Earth and Sky" is composed of poetry and statements and is scattered throughout uptown and will be up until Oct. 31.
Dashiell Coleman
Dash joined WFAE as a digital editor for news and engagement in 2019. Before that, he was a reporter for the Savannah Morning News in Georgia and the Gaston Gazette in Gastonia.
See stories by Dashiell Coleman


This project saw the formation of a new cosmetic surgery located in the heart of Tunbridge Wells, which has brought together market leading medical experts within one practice. Our client had an excellent track record in developing businesses and came to DMP with a detailed expectation and specification for the scheme.
The client had asked DMP to develop and achieve a practice that would utilise an existing building within the historic parts of the Lower Pantiles area in Tunbridge Wells as well as provide generous and comfortable accommodation for the practice's clients. Facility users would be coming from across the country for treatments and therefore the practice was required to be inviting and luxurious whilst meeting the stringent requirements of CQC (Care Quality Commission).
Prior to the redevelopment of Market House the property had been used as offices for many years: its layout was typical for a period property of its age and offered generous room sizes and a spacious basement. The sizes of the rooms and accommodation available meant that the design could allow for good movement throughout the building to create privacy for patients.
The client had already begun the process of marketing the cosmetic surgery and therefore an opening date had been set at an early stage. DMP were able to develop a scheme and tender the works swiftly in order that the project was underway as quickly as possible in order to achieve the summer opening date.
The client was keen to utilise his expertise and had sourced specialist suppliers with which DMP would discuss specifications and requirements, incorporating these into the detailed program of works.
The client was keen for the project to meet certain design requirements whilst adhering to strict medical practices required of a high quality cosmetic surgery and therefore DMP sourced specialist contractors to design and install feature walling and fittings.
DMP were involved at an early stage and worked alongside the client in designing and writing up specifications for the project. DMP's skilled CAD team were able to create plans for the property which illustrated general arrangements, plumbing and electrical routes, and the integration of specialist medical equipment.
The project management of the scheme became very detailed and the contractor was supplied with information on every aspect of the development. Site inspections and meetings with contractors and the client were conducted almost daily towards the latter stages of the development to ensure the client's expectations for opening were achieved.
The end result of this project has utilised an existing building and transformed it into a high quality surgery that is used by patients across the country. The cosmetic surgery is often seen on social media outlets and is known for its high standards.